ArticleFrontiers in neuroinformatics2016
Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Spatially integrated cortico-subcortical tracing data for analyses of rodent brain topographical organization.Scientific data · 2024Article
- Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration.Nature methods · 2023Article
- On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.PLoS computational biology · 2018Article
- Mapping Histological Slice Sequences to the Allen Mouse Brain Atlas Without 3D Reconstruction.Frontiers in neuroinformatics · 2018Article
- Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies.Frontiers in neuroanatomy · 2018Article
- Mapping and Analysis of the Connectome of Sympathetic Premotor Neurons in the Rostral Ventrolateral Medulla of the Rat Using a Volumetric Brain Atlas.Frontiers in neural circuits · 2017Article
- 3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas.Frontiers in neuroanatomy · 2016Article
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4 authors.
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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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