The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases

Burns, Gully A. P. C.; Cheng, Wei-Cheng; Thompson, Richard H.; Swanson, Larry W.

doi:10.1186/1471-2105-7-531

Cited by 15 publications

(18 citation statements)

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“…In a meta-analysis of 4,513 peer reviewed tract-tracing publications describing non-viral tract-tracing experiments in juvenile and adult rats we found 3,265 articles that provide connectivity data that are useful for a mesoscale connectome analysis (Schmitt et al 2012b;Burns and Cheng 2006;Burns et al 2008a, b;Swanson 2007b, 2010). Nearly 800 of these 3,265 publications are considered as the data core because they contain connectivity data of particular regions of the basal ganglia.…”

Section: Methodsmentioning

confidence: 97%

“…This software is a platformindependent generic framework developed in JAVA TM for advanced connectome analysis (Bullmore and Sporns 2009;Stam and Reijneveld 2007;Albert and Barabasi 2002;Sporns et al 2000aSporns et al , 2004, visual analytics (Ware 2013;Archambault et al 2008;Wong et al 2006a, b) and nervous system specific 3D-multiscale visualizations using stereotaxic coordinate systems (Hjornevik et al 2007;Gustafson et al 2004Gustafson et al , 2007Ju et al 2006;Bjaalie 2002). The advantage of neuroVIISAS is that different modalities like ontologies, digital atlasing, connectomics and neuron population simulation (Feng et al 2005;Burns et al 2006;Gewaltig and Diesmann 2007;Moore et al 2007;Niggemann et al 2008;Lee et al 2008;Gouws et al 2009) are integrated into one flexible framework (Martone et al 2004). The neuroVIISAS installation package can be downloaded from (http://neu roviisas.med.uni-rostock.de).…”

Section: Methodsmentioning

confidence: 99%

“…A further aim of this investigation was to collate the connectivity data (Burns and Cheng 2006) of all peerreviewed non-viral tract tracing publications (Bohland et al 2009a) in which connections of regions of the BG were described. Viral tract tracing was not considered in this work because some neurotrophic viruses propagate transsynaptically in a time-dependent manner.…”

Section: Introductionmentioning

confidence: 99%

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The connectome of the basal ganglia

et al. 2014

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The basal ganglia of the laboratory rat consist of a few core regions that are specifically interconnected by efferents and afferents of the central nervous system. In nearly 800 reports of tract-tracing investigations the connectivity of the basal ganglia is documented. The readout of connectivity data and the collation of all the connections of these reports in a database allows to generate a connectome. The collation, curation and analysis of such a huge amount of connectivity data is a great challenge and has not been performed before (Bohland et al. PloS One 4:e7200, 2009) in large connectomics projects based on meta-analysis of tract-tracing studies. Here, the basal ganglia connectome of the rat has been generated and analyzed using the consistent cross-platform and generic framework neuroVIISAS. Several advances of this connectome meta-study have been made: the collation of laterality data, the network-analysis of connectivity strengths and the assignment of regions to a hierarchically organized terminology. The basal ganglia connectome offers differences in contralateral connectivity of motoric regions in contrast to other regions. A modularity analysis of the weighted and directed connectome produced a specific grouping of regions. This result indicates a correlation of structural and functional subsystems. As a new finding, significant reciprocal connections of specific network motifs in this connectome were detected. All three principal basal ganglia pathways (direct, indirect, hyperdirect) could be determined in the connectome. By identifying these pathways it was found that there exist many further equivalent pathways possessing the same length and mean connectivity weight as the principal pathways. Based on the connectome data it is unknown why an excitation pattern may prefer principal rather than other equivalent pathways. In addition to these new findings the local graph-theoretical features of regions of the connectome have been determined. By performing graph theoretical analyses it turns out that beside the caudate putamen further regions like the mesencephalic reticular formation, amygdaloid complex and ventral tegmental area are important nodes in the basal ganglia connectome. The connectome data of this meta-study of tract-tracing reports of the basal ganglia are available for further network studies, the integration into neocortical connectomes and further extensive investigations of the basal ganglia dynamics in population simulations.

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Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The connectome of the basal ganglia

et al. 2014

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“…This process of hand-drawing maps into a standard brain atlas provides an accurate registration of anatomical data into a standard atlas-based framework when performed carefully and correctly by experts (Swanson, 2004; Burns et al, 2006). This representation is only the first step of the larger goal of building systems to understand the substructure of a connection’s fields of origin and termination in a knowledge engineering interface.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Synthesis with Maps of Neural Connectivity

Tallis¹,

Thompson²,

Russ³

et al. 2011

Front. Neuroinform.

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This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called “Knowledge Engineering from Experimental Design” (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

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“…A great advantage of this approach is the ability to stack in register and compare data patterns from different histological sections, or from different brains (Canteras et al, 1992), over the atlas level template in the layer manager of a computer graphics application like Adobe Illustrator™ (Swanson, 1998, 2001). In fact, the data layers can be stored in a database, and used as the foundation of a neuroanatomical map information repository (Dashti et al, 1997; Burns et al, 2006). However, current implementation of this approach relies on expert transfer of data from experimental brain sections to reference atlas levels, which is a complex, very time-consuming procedure (Swanson, 1998, 2004) that must deal with the many sources of error considered systematically in Section 3.…”

Section: Introductionmentioning

confidence: 99%

Comparing histological data from different brains: Sources of error and strategies for minimizing them

Simmons

Swanson

2009

Brain Research Reviews

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The recent development of brain atlases with computer graphics templates, and of huge databases of neurohistochemical data on the internet, has forced a systematic re-examination of errors associated with comparing histological features between adjacent sections of the same brain, between brains treated in the same way, and between brains from groups treated in different ways. The long-term goal is to compare as accurately as possible a broad array of data from experimental brains within the framework of reference atlases. Main sources of error, each of which ideally should be measured and minimized, include intrinsic biological variation, linear and nonlinear distortion of histological sections, plane of section differences between each brain, section alignment problems, and sampling errors. These variables are discussed, along with approaches to error estimation and minimization in terms of a specific example—the distribution of neuroendocrine neurons in the rat paraventricular nucleus. Based on the strategy developed here, the main conclusion is that the best long-term solution is a high-resolution 3D computer graphics model of the brain that can be sliced in any plane and used as the framework for quantitative neuroanatomy, databases, knowledge management systems, and structure-function modeling. However, any approach to the automatic annotation of neuroanatomical data—relating its spatial distribution to a reference atlas—should deal systematically with these sources of error, which reduce localization reliability.

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The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases

Cited by 15 publications

References 50 publications

The connectome of the basal ganglia

The connectome of the basal ganglia

Knowledge Synthesis with Maps of Neural Connectivity

Comparing histological data from different brains: Sources of error and strategies for minimizing them

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