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Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections

Overview of attention for article published in Frontiers in Neuroinformatics, April 2016
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Title
Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections
Published in
Frontiers in Neuroinformatics, April 2016
DOI 10.3389/fninf.2016.00011
Pubmed ID
Authors

Eszter A. Papp, Trygve B. Leergaard, Gergely Csucs, Jan G. Bjaalie

Abstract

Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 2%
Switzerland 1 2%
Unknown 47 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 37%
Student > Ph. D. Student 6 12%
Student > Bachelor 5 10%
Student > Master 4 8%
Professor 3 6%
Other 5 10%
Unknown 8 16%
Readers by discipline Count As %
Neuroscience 22 45%
Agricultural and Biological Sciences 4 8%
Computer Science 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Engineering 2 4%
Other 8 16%
Unknown 8 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 May 2016.
All research outputs
#13,975,135
of 22,862,742 outputs
Outputs from Frontiers in Neuroinformatics
#461
of 750 outputs
Outputs of similar age
#154,536
of 299,207 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#8
of 10 outputs
Altmetric has tracked 22,862,742 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 750 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,207 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.