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On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

ABSTRACT: This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 ?m meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.

SUBMITTER: Lee BC

PROVIDER: S-EPMC6324828 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

Lee Brian C BC Tward Daniel J DJ Mitra Partha P PP Miller Michael I MI

PLoS computational biology 20181226 12

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensi ...[more]

PMID: 30586384

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Project description:The pedal ganglion of the nudibranch gastropod Tritonia diomedea has been the focus of neurophysiological studies for more than 50 yr. These investigations have examined the neural basis of behaviors as diverse as swimming, crawling, reflex withdrawals, orientation to water flow, orientation to the earth's magnetic field, and learning. Despite this sustained research focus, most studies have confined themselves to the layer of neurons that are visible on the ganglion surface, leaving many neurons, which reside in deeper layers, largely unknown and thus unstudied. To facilitate work on such neurons, the present study used serial-section light microscopy to generate a detailed pictorial atlas of the pedal ganglion. One pedal ganglion was sectioned horizontally at 2-µm intervals and another vertically at 5-µm intervals. The resulting images were examined separately or combined into stacks to generate movie tours through the ganglion. These were also used to generate 3D reconstructions of individual neurons and rotating movies of digitally desheathed whole ganglia to reveal all surface neurons. A complete neuron count of the horizontally sectioned ganglion yielded 1,885 neurons. Real and virtual sections from the image stacks were used to reveal the morphology of individual neurons, as well as the major axon bundles traveling within the ganglion to and between its several nerves and connectives. Extensive supplemental data are provided, as well as a link to the Dryad Data Repository site, where the complete sets of high-resolution serial-section images can be downloaded. NEW & NOTEWORTHY Because of the large size and relatively low numbers of their neurons, gastropod mollusks are widely used for investigations of the neural basis of behavior. Most studies, however, focus on the neurons visible on the ganglion surface, leaving the majority, located out of sight below the surface, unexamined. The present light microscopy study generates the first detailed visual atlas of all neurons of the highly studied Tritonia pedal ganglion.

Dataset Information

On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

Publications

On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

Similar Datasets

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