Rolf J. F. Ypma

Bio: Rolf J. F. Ypma is an academic researcher from Netherlands Forensic Institute. The author has contributed to research in topics: Population & Connectome. The author has an hindex of 13, co-authored 22 publications receiving 817 citations. Previous affiliations of Rolf J. F. Ypma include Utrecht University & University of Cambridge.

Wiring cost and topological participation of the mouse brain connectome.

Abstract: Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of “wiring cost” explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain. We estimated brain-wide weights, distances, and wiring costs of axonal projections and performed a multiscale topological and spatial analysis of the resulting weighted and directed mouse brain connectome. Our analysis showed that the mouse connectome has small-world properties, a hierarchical modular structure, and greater-than-minimal wiring costs. High-participation hubs of this connectome mediated communication between functionally specialized and anatomically localized modules, had especially high wiring costs, and closely corresponded to regions of the default mode network. Analyses of independently acquired histological and gene-expression data showed that nodal participation colocalized with low neuronal density and high expression of genes enriched for cognition, learning and memory, and behavior. The mouse connectome contains high-participation hubs, which are not explained by wiring-cost minimization but instead reflect competitive selection pressures for integrated network topology as a basis for higher cognitive and behavioral functions.

Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

Abstract: Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data

Abstract: Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes.

Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery

Abstract: Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a precis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.

Genetic Data Provide Evidence for Wind-Mediated Transmission of Highly Pathogenic Avian Influenza

Abstract: Outbreaks of highly pathogenic avian influenza in poultry can cause severe economic damage and represent a public health threat. Development of efficient containment measures requires an understanding of how these influenza viruses are transmitted between farms. However, the actual mechanisms of interfarm transmission are largely unknown. Dispersal of infectious material by wind has been suggested, but never demonstrated, as a possible cause of transmission between farms. Here we provide statistical evidence that the direction of spread of avian influenza A(H7N7) is correlated with the direction of wind at date of infection. Using detailed genetic and epidemiological data, we found the direction of spread by reconstructing the transmission tree for a large outbreak in the Netherlands in 2003. We conservatively estimate the contribution of a possible wind-mediated mechanism to the total amount of spread during this outbreak to be around 18%.

ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data

Abstract: Summary We present an r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees. ggtree can read more tree file formats than other softwares, including newick, nexus, NHX, phylip and jplace formats, and support visualization of phylo, multiphylo, phylo4, phylo4d, obkdata and phyloseq tree objects defined in other r packages. It can also extract the tree/branch/node-specific and other data from the analysis outputs of beast, epa, hyphy, paml, phylodog, pplacer, r8s, raxml and revbayes software, and allows using these data to annotate the tree. The package allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion. A two-dimensional tree can be drawn by scaling the tree width based on an attribute of the nodes. A tree can be annotated with an associated numerical matrix (as a heat map), multiple sequence alignment, subplots or silhouette images. The package ggtree is released under the artistic-2.0 license. The source code and documents are freely available through bioconductor (http://www.bioconductor.org/packages/ggtree).

Communication dynamics in complex brain networks.

Abstract: Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.

The Cerebral Cortex of Man

Abstract: Cerebral angiography was inaugurated by Egas Rfoniz of Lisbon in 1926 and has now become one of the standard diagnostic methods of cerebral lesions. Lima’s book is the first systematic treatise on this subject published by the Portuguese school in the English language. The author has been associated as neurosurgeon with RIoniz for more than twenty years, and the combined material of these two investigators comprises more than two thousand angiographies, a numbcr unsurpassed anywhere. Lima corroborates, amplifies and in some rases revises Moniz’ fundamental work which had bcen published previously in the French and German literature. The subject matter is covered in $ell organized and critical fashion, but unfortunately too little attention is paid to the numerous publications on angiography which appeared in Scandinavian, American and German literature, especially after the war. After reviewing the history of angiography, its technique is discussed. Lima advocates surgical exposure of the common carotid artery low a t the neck in preference to the percutaneous technique now practiced in many centers. He performs carotid angiography bilaterally in one sitting under local anesthesia and obtains routinely three lateral films for each injection (the first film to show the arterial phase, the second the early venous phase, and the third the late venous phase). AP projections are rarely used, and stereoscopic films never taken. The next chapter deals in detail with the normal arteriogram and phlebogram obtained by vertebral and carotid injection. Then follows a description of the typical displacements and deformities of cerebral vessels caused by intracranial tumors and, hydrocephalus. I n the next section the angioarchitectonic pattern of cerebral tumors is described. This chapter is particularly interesting and informative since Lima has done much original work on the circulation of meningiomas. The typical double blood supply of meningiomas through branches of the external and internal carotid arteries is discussed, and also the relation of ,angiographic pictures to modification of the circulation time is analyzed. The chapter on cerebral aneurysm is relatively brief but vascular malformations are discussed at length. Angiography offers an important aid i n the diagnosis of cerebral vascular occlusion. Special attention is given to the not uncommon thrombosis of the cervical portion of the carotid artery. This condition produces a characteristic clinical syndrome and can be easily recognized by low cervical carotid angiography.

Small-World Brain Networks Revisited.

Abstract: It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more b...

Betweenness Centrality in Large Complex Networks

Abstract: We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent $\eta$. We find that for trees or networks with a small loop density $\eta=2$ while a larger density of loops leads to $\eta<2$. For scale-free networks characterized by an exponent $\gamma$ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent $\delta$. We show that this exponent $\delta$ must satisfy the exact bound $\delta\geq (\gamma+1)/2$. If the scale free network is a tree, then we have the equality $\delta=(\gamma+1)/2$.