Rembrandt Bakker

CoCoMac 2.0 and the future of tract-tracing databases.

The CoCoMac database contains the results of several hundred published axonal tract-tracing studies in the macaque monkey brain. The combined results are used for constructing the macaque macro-connectome. Here we discuss the redevelopment of CoCoMac and compare it to six connectome-related projects: two online resources that provide full access to raw tracing data in rodents, a connectome viewer for advanced 3D graphics, a partial but highly detailed rat connectome, a brain data management system that generates custom connectivity matrices, and a software package that covers the complete pipeline from connectivity data to large-scale brain simulations. The second edition of CoCoMac features many enhancements over the original. For example, a search wizard is provided for full access to all tables and their nested dependencies. Connectivity matrices can be computed on demand in a user-selected nomenclature. A new data entry system is available as a preview, and is to become a generic solution for community-driven data entry in manually collated databases. We conclude with the question whether neuronal tracing will remain the gold standard to uncover the wiring of brains, thereby highlighting developments in human connectome construction, tracer substances, polarized light imaging, and serial block-face scanning electron microscopy.

The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content.

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays.

Hundreds of brain maps in one atlas: Registering coordinate-independent primate neuro-anatomical data to a standard brain

Non-invasive measuring methods such as EEG/MEG, fMRI and DTI are increasingly utilised to extract quantitative information on functional and anatomical connectivity in the human brain. These methods typically register their data in Euclidean space, so that one can refer to a particular activity pattern by specifying its spatial coordinates. Since each of these methods has limited resolution in either the time or spatial domain, incorporating additional data, such as those obtained from invasive animal studies, would be highly beneficial to link structure and function. Here we describe an approach to spatially register all cortical brain regions from the macaque structural connectivity database CoCoMac, which contains the combined tracing study results from 459 publications (http://cocomac.g-node.org). Brain regions from 9 different brain maps were directly mapped to a standard macaque cortex using the tool Caret (Van Essen and Dierker, 2007). The remaining regions in the CoCoMac database were semantically linked to these 9 maps using previously developed algebraic and machine-learning techniques (Bezgin et al., 2008; Stephan et al., 2000). We analysed neural connectivity using several graph-theoretical measures to capture global properties of the derived network, and found that Markov Centrality provides the most direct link between structure and function. With this registration approach, users can query the CoCoMac database by specifying spatial coordinates. Availability of deformation tools and homology evidence then allow one to directly attribute detailed anatomical animal data to human experimental results.

Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates.

BACKGROUNDnAssignment of anatomical reference is a key step in integration of the rapidly expanding collection of rodent brain data. Landmark-based registration facilitates spatial anchoring of diverse types of data not suitable for automated methods operating on voxel-based image information.nnnNEW TOOLnHere we propose a standardized set of anatomical landmarks for registration of whole brain imaging datasets from the mouse and rat brain, and in particular for integration of experimental image data in Waxholm Space (WHS).nnnRESULTSnSixteen internal landmarks of the C57BL/6J mouse brain have been reliably identified: by different individuals, independent of their experience in anatomy; across different MRI contrasts (T1, T2, T2(*)) and other modalities (Nissl histology and block-face anatomy); in different specimens; in different slice acquisition angles; and in different image resolutions. We present a registration example between T1-weighted MRI and the mouse WHS template using these landmarks and reaching fairly high accuracy. Landmark positions identified in the mouse WHS template are shared through the Scalable Brain Atlas, accompanied by graphical and textual guidelines for locating each landmark. We identified 14 of the 16 landmarks in the WHS template for the Sprague Dawley rat.nnnCOMPARISON WITH EXISTING METHODSnThis landmark set can withstand substantial differences in acquisition angle, imaging modality, and is less vulnerable to subjectivity.nnnCONCLUSIONSnThis facilitates registration of multimodal 3D brain data to standard coordinate spaces for mouse and rat brain taking a step toward the creation of a common rodent reference system; raising data sharing to a qualitatively higher level.

VisNEST — Interactive analysis of neural activity data

The aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scalce interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.

Feeding the human brain model

The goal of the Human Brain Project is to develop, during the next decade, an infrastructure capable of simulating a draft human brain model based on available experimental data. One of the key issues is therefore to integrate and make accessible the experimental data necessary to constrain and fully specify this model. The required data covers many different spatial scales, ranging from the molecular scale to the whole brain and these data are obtained using a variety of techniques whose measurements may not be directly comparable. Furthermore, these data are incomplete, and will remain so at least for the coming decade. Here we review new neuroinformatics techniques that need to be developed and applied to address these issues.

Macaque structural connectivity revisited: CoCoMac 2.0

CoCoMac (cocomac.org) is a large data base on structural connectivity in the Macaque brain, based on over 450 published axonal tracing studies [1]. This huge curation effort took place under the guidance of Rolf Kotter, and provided data to numerous brain network analysis and modeling studies. While working on a major new release of the database, Rolf Kotter sadly passed away in June 2010. We are committed to foster the further development of CoCoMac and are gradually releasing the newly developed web interface at the CoCoMac 2.0 server hosted at the German INCF Node (cocomac.g-node.org). n nMacaque structural connectivity data is relevant for uncovering the large-scale human connectome and increasingly plays a role in constraining neuronal network models that link the connectivity structure to activity dynamics and network function. With recent advances in computer hardware and simulation software [2], brain-scale simulations with millions of spiking neurons become feasible, accounting simultaneously for the macroscopic and the microscopic structure of cortical networks. These models promote the integration of local micro circuitry and long-range connectivity data on the level of the cell-type specificity of connections. Such level of detail cannot be provided by diffusion MRI-based techniques: for layer-specificity, intracortical resolution and directionality, one has to fall back to axonal tracing experiments. Combining these into a complete picture for the entire brain is an enormous challenge, as it involves experiments that have been measured in thousands of individual brains over the course of a century. n nSince the spatial coordinates of tracing injections are undocumented in most publications, CoCoMac has had no other choice than to describe connectivity completely in terms of named brain regions: ‘region A has axonal projections to region B’. A nomenclature mapping service known as ORT [3] translates named brain regions in older brain atlases to newer ones and vice versa. Producing correct nomenclature mappings is of crucial importance. Incorrect or imprecise use of nomenclature in the literature leads to conflicting (chains of) mapping statements, and to errors in the resulting connectivity. CoCoMac 2.0 detects for each mapping statement whether conflicting versions exist, and uses Bayesian reasoning to eliminate the inconsistent literature statements causing the conflicts. n nFor data exchange with MRI-based techniques, CoCoMac needs to attach spatial coordinates to its connectivity data. This is achieved by integrating CoCoMac with the INCF Scalable Brain Atlas (SBA, scalablebrainatlas.incf.org/cocomac) [4]. This web-based tool interactively displays structural connectivity in a spatial reference framework, and supports a number of commonly used brain atlases. The SBA provides a point-and-click interface to the low level text-based services at the CoCoMac server.

Transmission of X-ray-induced reciprocal translocations in normal male mice and in male mice with a reduced sperm count due to translocation homozygosity

Normal (+/+) and translocation T(1; 11.13S)70H homozygous (T/T) male mice received 2 X 2.5 Gy X-rays with a 24-h interval. After 120 days, the frequency of late diplotene-metaphase I spermatocytes with translocation multivalents was 14.1% for +/+ and 13.7% for T/T males, respectively, in one group of animals of each type. The difference is not significant. A second group was allowed to sire progeny for 60 days with 2 normal females per week. Reciprocal translocations detectable at diakinesis/metaphase I were observed in 2.5% of the 395 male progeny from the irradiated +/+ fathers, and in 2.9% of the 489 male progeny from the irradiated T/T fathers. This leads to a pooled estimated transmission of 0.81 +/- 0.19. Translocations induced in the long 11.13 metacentric chromosome were not transmitted with a different frequency. The rate of heritable induced translocations in this study was 5.4 X 10(-5)/rad/gamete. On the basis of the data of Generoso et al. (1984) for the frequency of the heritable spontaneous translocations in male mice, it is concluded that, because of their low doubling dose (3.3-4.6 rad), the spontaneous translocations are probably of postmeiotic origin.

The missing link: Predicting connectomes from noisy and partially observed tract tracing data

Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a ‘latent space model’ that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies.

Auditory-prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams

Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.