人类脑计划与神经信息学

了解脑及其功能是21世纪科学的重大挑战之一。神经信息学是神经科学与信息科学相结合的交叉学科。目前的“人类脑计划”旨在加强脑功能的基础研究,并开发用于分析、整合、合成、建模、模拟与提供各种数据的工具。中国应参与人类脑计划,为发展神经信息学作出贡献。     

Computational neuroscience and neuroinformatics: Recent progress and resources

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 ± 8.1 billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. ‘Computational neuroscience’ which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by ‘neuroinformatics’ approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-of-the-art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain–computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.     

神经信息学的原理与展望

神经信息学是研究神经系统信息的载体形式,神经信息的产生、传输、加工、编码、存储与提取机理,以及建立神经数据库系统的科学。它是脑科学,信息科学和计算机科学相互交叉的边缘学科。神经信息学可分为分子神经信息学和系统神经信息学两个层次。神经信息编码可分为神经元脉冲序列的数字编码和突触联结权重编码两种编码方式。对21世纪神经信息学可能取得的新进展进行分析和预测,并论证开展人类神经组计划（HuNP）和建立神经数据库系统的必要性与可行性。对人类神经计划与人类脑计划的异同步,进行比较和讨论。     

Neuroinformatics Research for Vision Science: NRV project

The NRV project (Neuroinformatics Research for Vision Science) is the first project in Japan started in 1999 under the Science and Technology Agency of Japan, aimed at building the foundation of neuroinformatics research. Because of the wealth of data on the visual system, the NRV project will use vision research to promote experimental, theoretical and technical research as a pilot study on neuroinformatics. Details can be found at: http://www.neuroinformatics.gr.jp/.     

''The surface management system'' (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis.

Surface reconstructions of the cerebral cortex are increasingly widely used in the analysis and visualization of cortical structure, function and connectivity. From a neuroinformatics perspective, dealing with surface-related data poses a number of challenges. These include the multiplicity of configurations in which surfaces are routinely viewed (e.g. inflated maps, spheres and flat maps), plus the diversity of experimental data that can be represented on any given surface. To address these challenges, we have developed a surface management system (SuMS) that allows automated storage and retrieval of complex surface-related datasets. SuMS provides a systematic framework for the classification, storage and retrieval of many types of surface-related data and associated volume data. Within this classification framework, it serves as a version-control system capable of handling large numbers of surface and volume datasets. With built-in database management system support, SuMS provides rapid search and retrieval capabilities across all the datasets, while also incorporating multiple security levels to regulate access. SuMS is implemented in Java and can be accessed via a Web interface (WebSuMS) or using downloaded client software. Thus, SuMS is well positioned to act as a multiplatform, multi-user 'surface request broker' for the neuroscience community.     

The McGill-Mouse-Miniscope platform: A standardized approach for high-throughput imaging of neuronal dynamics during behavior

Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remains one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behaviors, and file structures. Discrepancies between experimental protocols present key challenges in comparing data between laboratories and across different brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Miniscope (M3) platform is an initiative to combine miniscope calcium imaging with standardized touchscreen-based animal behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.     

Application of microarray technology in primate behavioral neuroscience research

Gene expression profiling of brain tissue samples applied to DNA microarrays promises to provide novel insights into the neurobiological bases of primate behavior. The strength of the microarray technology lies in the ability to simultaneously measure the expression levels of all genes in defined brain regions that are known to mediate behavior. The application of microarrays presents, however, various limitations and challenges for primate neuroscience research. Low RNA abundance, modest changes in gene expression, heterogeneous distribution of mRNA among cell subpopulations, and individual differences in behavior all mandate great care in the collection, processing, and analysis of brain tissue. A unique problem for nonhuman primate research is the limited availability of species-specific arrays. Arrays designed for humans are often used, but expression level differences are inevitably confounded by gene sequence differences in all cross-species array applications. Tools to deal with this problem are currently being developed. Here we review these methodological issues, and provide examples from our experiences using human arrays to examine brain tissue samples from squirrel monkeys. Until species-specific microarrays become more widely available, great caution must be taken in the assessment and interpretation of microarray data from nonhuman primates. Nevertheless, the application of human microarrays in nonhuman primate neuroscience research recovers useful information from thousands of genes, and represents an important new strategy for understanding the molecular complexity of behavior and mental health.     

单神经元分辨水平的小鼠全脑网络可视化

脑科学是当今国际科技研究的前沿领域。脑是最复杂的器官,其中尚有诸多重大的基础科学问题有待解决。开展脑科学研究需多学科人员协同攻关,对于建立新学科将有极大的促进作用,对于人类健康和社会发展具有巨大的推动作用。简述了神经科学在结构成像方面的基础性需求,介绍了小鼠全脑可视化的发展历程以及近几年的代表性研究,并展望了全脑可视化研究的发展趋势,对可能存在的难点予以说明。     

Towards the "baby connectome": mapping the structural connectivity of the newborn brain

Defining the structural and functional connectivity of the human brain (the human "connectome") is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores.     

Evolutionary coherence of the mammalian amygdala

Despite great interest in the role of the amygdala in animal and human behaviour, its very existence as a structurally and functionally unified brain component has been questioned, on the grounds that cell groups within it display divergent pharmacological and connectional characteristics. We argue that the question of whether particular brain nuclei constitute a valid structural and functional unit is inherently an evolutionary question, and we present a method for answering it. The method involves phylogenetic analysis of comparative data to determine whether or not separate regions of the putative brain structure show statistically correlated evolution. We find that, in three separate groups of mammals (primates and two groups of insectivores), evolutionary changes in the volumes of amygdala components are strongly correlated, even after controlling for volumetric change in a wide range of limbic and other brain structures. This allows us to reject the strong claim that the amygdala is neither a structural nor a functional unit, and demonstrates the importance of evolutionary analysis in resolving such issues in systems neuroscience.     

Bioinformatics: perspectives for the future

I give here a very personal perspective of Bioinformatics and its future, starting by discussing the origin of the term (and area) of bioinformatics and proceeding by trying to foresee the development of related issues, including pattern recognition/data mining, the need to reintegrate biology, the potential of complex networks as a powerful and flexible framework for bioinformatics and the interplay between bio- and neuroinformatics. Human resource formation and market perspective are also addressed. Given the complexity and vastness of these issues and concepts, as well as the limited size of a scientific article and finite patience of the reader, these perspectives are surely incomplete and biased. However, it is expected that some of the questions and trends that are identified will motivate discussions during the IcoBiCoBi round table (with the same name as this article) and perhaps provide a more ample perspective among the participants of that conference and the readers of this text.     

Beyond phrenology,at last

Although integration is a widely acknowledged goal in neuroscience, our approach to the function of biological entities often places boundaries that defy integration. Mapping across systems - from the genome to cognitive function - will require innovative methods that can identify every contributing component to a function, and instantaneously scale numerous changes in large data sets to consequences over the entire biological hierarchy.     

International perspectives on engaging the public in neuroethics

With an ever-increasing understanding of the brain mechanisms associated with core human attributes and values, there is an increasing public interest in the results of neuroscience research and the ways in which that new knowledge will be used. Here, we present perspectives on engaging the public on these issues on an international scale, the role of the media, and prospects for the new field of neuroethics as both a focus and a driver of these efforts.     

The Problem of Thresholding in Small-World Network Analysis

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues.     

Functional implications of hippocampal adult neurogenesis in intellectual disabilities

The development of strategies capable to promote nervous system plasticity in adulthood is nowadays an important aim in neuroscience to improve not only cognitive abilities but also to ameliorate pathological dysfunctions. Several studies have demonstrated that adult neurogenesis is regulated by many physiological and pathological stimuli at almost every stage, from proliferation of neuronal precursors until integration and activation of newly formed neurons in the preexisting network. We review the process of generating functional neurons from precursors in the adult brain and its implications in intellectual disability disorders.     

Neuronal ensemble dynamics in social memory

A large body of evidence suggests that cognitive functions rely on the coordination of ensembles of neurons across brain circuits. One example is social memory, the ability to recognize and remember other conspecifics. A broad range of brain regions have been implicated in social behaviors and memory processes. At the single-cell level, neurons from different brain areas have responded to specific social features. The coordination of these ensembles both within a region and across structures is required to support social memory and decision-making. The synchronous activation of these neuronal ensembles could allow for the integration of different aspects of a social episode into a unified representation of experience. In this review, recent results on the circuit basis and physiological mechanisms of social memory are discussed, from a systems neuroscience perspective. An integrative framework of the neuronal ensemble dynamics supporting this fundamental cognitive ability is proposed.     

Fusing EEG and fMRI based on a bottom-up model: inferring activation and effective connectivity in neural masses

The elucidation of the complex machinery used by the human brain to segregate and integrate information while performing high cognitive functions is a subject of imminent future consequences. The most significant contributions to date in this field, known as cognitive neuroscience, have been achieved by using innovative neuroimaging techniques, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), which measure variations in both the time and the space of some interpretable physical magnitudes. Extraordinary maps of cerebral activation involving function-restricted brain areas, as well as graphs of the functional connectivity between them, have been obtained from EEG and fMRI data by solving some spatio-temporal inverse problems, which constitutes a top-down approach. However, in many cases, a natural bridge between these maps/graphs and the causal physiological processes is lacking, leading to some misunderstandings in their interpretation. Recent advances in the comprehension of the underlying physiological mechanisms associated with different cerebral scales have provided researchers with an excellent scenario to develop sophisticated biophysical models that permit an integration of these neuroimage modalities, which must share a common aetiology. This paper proposes a bottom-up approach, involving physiological parameters in a specific mesoscopic dynamic equations system. Further observation equations encapsulating the relationship between the mesostates and the EEG/fMRI data are obtained on the basis of the physical foundations of these techniques. A methodology for the estimation of parameters from fused EEG/fMRI data is also presented. In this context, the concepts of activation and effective connectivity are carefully revised. This new approach permits us to examine and discuss some future prospects for the integration of multimodal neuroimages.     

Multisensory integration: methodological approaches and emerging principles in the human brain.

Understanding the conditions under which the brain integrates the different sensory streams and the mechanisms supporting this phenomenon is now a question at the forefront of neuroscience. In this paper, we discuss the opportunities for investigating these multisensory processes using modern imaging techniques, the nature of the information obtainable from each method and their benefits and limitations. Despite considerable variability in terms of paradigm design and analysis, some consistent findings are beginning to emerge. The detection of brain activity in human neuroimaging studies that resembles multisensory integration responses at the cellular level in other species, suggests similar crossmodal binding mechanisms may be operational in the human brain. These mechanisms appear to be distributed across distinct neuronal networks that vary depending on the nature of the shared information between different sensory cues. For example, differing extents of correspondence in time, space or content seem to reliably bias the involvement of different integrative networks which code for these cues. A combination of data obtained from haemodynamic and electromagnetic methods, which offer high spatial or temporal resolution respectively, are providing converging evidence of multisensory interactions at both "early" and "late" stages of processing--suggesting a cascade of synergistic processes operating in parallel at different levels of the cortex.     

多通道神经信号分析方法及其应用

在神经科学研究中,多通道记录方法被普遍应用在对神经元群体活动特性的研究中.通过分析多个神经元的活动,可以了解神经系统协同编码外界信息的规则以及大脑实现各项功能的机制.为了挖掘出多通道神经信号中携带的信息及其潜在的相关性,需要合适的计算方法辅助对神经元放电活动进行解码.本文回顾了多通道神经信号分析中的一些常见方法,以及它...