Asian promise: the state and future of collaborations in neuroscience

As investment in science and technology continues to grow in Asia, countries such as China, Japan and Singapore are witnessing great improvements in their neuroscience research environments; this is reflected in the opening of new research institutions and in the influx of neuroscientists trained abroad. Collaborative projects between researchers in these countries and laboratories in the United States and Europe are not only helping to shape these institutions, they are also leading to a surge in high-quality publications in both basic and translational neuroscience, resulting in increasing international recognition. Nature Reviews Neuroscience asks four neuroscientists about their collaborative experiences and the impact that such collaborations are having on neuroscience research.     

中国神经科学十年进程

为了迎接21世纪的挑战,推动我国神经科学基础研究的快速发展,中国科学院于1999年底在上海成立了中国科学院神经科学研究所(下简称上海神经所).经过十余年的发展,上海神经所取得了突出的成就,跻身国际水平研究所行列,为我国神经科学发展做出了重要贡献。     

Estimating the directed information to infer causal relationships in ensemble neural spike train recordings

Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures—most of which are variants of Granger causality—with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger’s original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.     

Causal measures of structure and plasticity in simulated and living neural networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify "causal" relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.     

Cortical dynamic tuning: the role of intrinsic connections, as revealed by modeling

A current challenge in computational neuroscience is to elucidate the role of cortical circuitry in information processing and in generating motor output. Our understanding of the functional significance of specifically organized feedback connections is progressing rapidly as researchers establish the equivalence of theoretical models to biological neural circuits. Modeling studies of different neural structures, along with quantitative comparisons of model performance to biological data, have recently helped to identify the basic features of synaptic connectivity that may play important roles in cortical operations.     

喀斯特常绿落叶阔叶林死亡个体空间分布格局及生境关联

树木死亡在森林动态中发挥着重要作用, 环境因子是影响树木死亡的重要因素。为了阐明喀斯特常绿落叶阔叶林死亡个体空间分布格局及其与环境的相关性, 本研究以木论25 ha森林动态监测样地的两次木本植物调查数据为基础, 分析了死亡个体的物种组成、径级结构、分布格局及其与生境的关联。结果表明, 样地内共有死亡个体17,306株, 隶属57科130属194种; 其中, 死亡率排名前三的科和属分别为八角枫科、蔷薇科、大风子科以及山麻杆属(Alchornea)、火棘属(Pyracantha)、八角枫属(Alangium); 在物种水平上, 山麻杆(Alchornea trewioides)、毛桐(Mallotus barbatus)、野桐(Mallotus japonicus)等死亡率较高。样地内死亡个体的平均胸径为3.83 cm, 最大47.11 cm; 径级分布呈倒“J”型。从分布格局来看, 样地内大部分死亡个体在0-50 m的尺度上呈聚集分布, 部分大径级树木死亡个体趋向于随机分布。生境关联分析发现, 个体死亡率与山顶、陡坡、缓坡、洼地呈正关联的物种数分别为41种、13种、41种和38种, 呈负关联的物种数分别为38种、67种、33种和10种, 与各生境无关联的物种数分别为4种、3种、9种和35种; 多度排名前20的物种其个体死亡率均与4种生境类型呈现出正关联或者负关联; 不同径级个体死亡率都与山顶生境呈正关联, 与缓坡生境呈负关联, 其中5 cm ≤ DBH < 10 cm和10 cm ≤ DBH < 15 cm个体死亡率均与洼地生境呈正关联; DBH ≥ 15 cm个体死亡率与陡坡生境呈正关联, 与洼地生境呈负关联。在喀斯特亚热带常绿落叶阔叶林中, 死亡个体空间分布格局总体表现为聚集分布, 树木死亡受生境作用的影响较大。     

Encoding brain network response to free viewing of videos

A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain’s responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain’s activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.     

SGD: Saccharomyces Genome Database.

The Saccharomyces Genome Database (SGD) provides Internet access to the complete Saccharomyces cerevisiae genomic sequence, its genes and their products, the phenotypes of its mutants, and the literature supporting these data. The amount of information and the number of features provided by SGD have increased greatly following the release of the S.cerevisiae genomic sequence, which is currently the only complete sequence of a eukaryotic genome. SGD aids researchers by providing not only basic information, but also tools such as sequence similarity searching that lead to detailed information about features of the genome and relationships between genes. SGD presents information using a variety of user-friendly, dynamically created graphical displays illustrating physical, genetic and sequence feature maps. SGD can be accessed via the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/     

Time-variant estimation of directed influences during Parkinsonian tremor

The inference of interaction structures in multidimensional time series is a major challenge not only in neuroscience but in many fields of research. To gather information about the connectivity in a network from measured data, several parametric as well as non-parametric approaches have been proposed and widely examined. Today a lot of interest is focused on the evolution of the network connectivity in time which might contain information about ongoing tasks in the brain or possible dynamic dysfunctions. Therefore an extension of the current approaches towards time-resolved analysis techniques is desired. We present a parametric approach for time variant analysis, test its performance for simulated data, and apply it to real-world data.     

Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity

The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular studies, we currently have characteristic connectivity and gene expression signatures for most of its neurons. By using two complementary analysis assays we show that the expression signature of a neuron carries significant information about its synaptic connectivity signature, and identify a list of putative genes predicting neural connectivity. The current study rigorously quantifies the relation between gene expression and synaptic connectivity signatures in the C. elegans nervous system and identifies subsets of neurons where this relation is highly marked. The results presented and the genes identified provide a promising starting point for further, more detailed computational and experimental investigations.     

(M,R)-systems, (P,M,C)-nets, hierarchical decay, and biological aging: reminiscences of Robert Rosen

I have the pleasure to present a number of personal experiences that I had with Robert Rosen, both as his student and as a research colleague, and I will describe how this affected my academic career over the past decades. As a matter of fact, Rosen's work with (M,R)-systems as well as his continuing mentorship guided me into my own research in gerontology and geriatrics. Amazingly, this still continues to affect my work in complexity theory after 30 years.     

AliBaba: PubMed as a graph

The biomedical literature contains a wealth of information on associations between many different types of objects, such as protein-protein interactions, gene-disease associations and subcellular locations of proteins. When searching such information using conventional search engines, e.g. PubMed, users see the data only one-abstract at a time and 'hidden' in natural language text. AliBaba is an interactive tool for graphical summarization of search results. It parses the set of abstracts that fit a PubMed query and presents extracted information on biomedical objects and their relationships as a graphical network. AliBaba extracts associations between cells, diseases, drugs, proteins, species and tissues. Several filter options allow for a more focused search. Thus, researchers can grasp complex networks described in various articles at a glance. AVAILABILITY: http://alibaba.informatik.hu-berlin.de/     

Describing the organization of dominance relationships by dominance-directed tree method

Methods to describe dominance hierarchies are a key tool in primatology studies. Most current methods are appropriate for analyzing linear and near-linear hierarchies; however, more complex structures are common in primate groups. We propose a method termed "dominance-directed tree." This method is based on graph theory and set theory to analyze dominance relationships in social groups. The method constructs a transitive matrix by imposing transitivity to the dominance matrix and produces a graphical representation of the dominance relationships, which allows an easy visualization of the hierarchical position of the individuals, or subsets of individuals. The method is also able to detect partial and complete hierarchies, and to describe situations in which hierarchical and nonhierarchical principles operate. To illustrate the method, we apply a dominance tree analysis to artificial data and empirical data from a group of Cebus apella.     

SURF’s UP! — Protein classification by surface comparisons

Large-scale genome sequencing and structural genomics projects generate numerous sequences and structures for 'hypothetical' proteins without functional characterizations. Detection of homology to experimentally characterized proteins can provide functional clues, but the accuracy of homology-based predictions is limited by the paucity of tools for quantitative comparison of diverging residues responsible for the functional divergence. SURF'S UP! is a web server for analysis of functional relationships in protein families, as inferred from protein surface maps comparison according to the algorithm. It assigns a numerical score to the similarity between patterns of physicochemical features(charge, hydrophobicity) on compared protein surfaces. It allows recognizing clusters of proteins that have similar surfaces, hence presumably similar functions. The server takes as an input a set of protein coordinates and returns files with "spherical coordinates" of proteins in a PDB format and their graphical presentation, a matrix with values of mutual similarities between the surfaces, and the unrooted tree that represents the clustering of similar surfaces, calculated by the neighbor-joining method. SURF'S UP! facilitates the comparative analysis of physicochemical features of the surface, which are the key determinants of the protein function. By concentrating on coarse surface features, SURF'S UP! can work with models obtained from comparative modelling. Although it is designed to analyse the conservation among homologs, it can also be used to compare surfaces of non-homologous proteins with different three-dimensional folds, as long as a functionally meaningful structural superposition is supplied by the user. Another valuable characteristic of our method is the lack of initial assumptions about the functional features to be compared. SURF'S UP! is freely available for academic researchers at http://asia.genesilico.pl/surfs_up/.     

Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations

Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. Further analysis reveals that these graphical integrity issues are significantly more prevalent in some research fields, such as psychology and computer science, and some regions of the globe. Additionally, we find no temporal and seniority trends in violations. Finally, apart from openly releasing our large annotated dataset and method, we discuss how computational research integrity could be part of peer-review and the publication processes.     

Analysis of the connectional organization of neural systems associated with the hippocampus in rats

The hippocampus of the rat enjoys a central significance for researchers interested in the neural mechanisms of memory and spatial information processing. Many of the theoretical models advanced to explain function in this system, however, do not reflect the wealth of information on the connectivity of these structures, and employ greatly simplified treatments of its complex connectivity. We were interested in whether a more analytical approach, which begins with analysis of the connectivity of the system, might provide insights complementary to those derived by synthetic models. Accordingly, we collated detailed neuroanatomical information about the connectivity of the hippocampal system in the rat, and analysed the resulting data. Analyses of connectivity based on a variety of different analytical techniques have recently been used to elucidate the global organization of other systems in the macaque and cat, and have given rise to successful predictions. We applied non-metric multidimensional scaling and non-parametric cluster analysis to our summary matrix of connection data. The analyses produced organizational schemes that were consistent with known physiological properties and provided the basis for making tentative predictions of the further structures that may contain 'place' and 'head-direction' cells, which structures we identify. The consistency between the analyses of connectivity and the distribution of physiological properties across the system suggests that functional relationships are constrained by the organization of the connectivity of the system, and so that structure and function are linked at the systems level.     

Information theoretic interpretation of frequency domain connectivity measures

In order to provide adequate multivariate measures of information flow between neural structures, modified expressions of partial directed coherence (PDC) and directed transfer function (DTF), two popular multivariate connectivity measures employed in neuroscience, are introduced and their formal relationship to mutual information rates are proved.