Biology subjects

Lee, B. C.

Publications and source records attributed to Lee, B. C..

3 recordsLinked to original sources

bioRxiv2018

A High-throughput Neurohistological Pipeline for Brain-Wide Mesoscale Connectivity Mapping of the Common Marmoset

Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. MRI based methods offer whole brain coverage, but remain indirect in the approach to connectivity mapping. Recent progress has been made in directly mapping whole-brain connectivity architecture in the mouse at the mesoscopic scale. The basic approach uses tracer injections systematically placed on a grid of locations spanning the brain and computational analysis of the resulting whole brain data sets. Scaling this approach to bigger primate brains poses nontrivial technical challenges. Here we present an integrated neurohistological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common Marmoset (Callithrix jacchus). Individual brains are sectioned into [~]1700 20{micro}m sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labelled sections. Combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to overcome the significant individual variation exhibited by Marmosets to obtain routine and high quality maps to a common atlas framework. This will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain. While component instruments or protocols may be available from previous work, we believe that this is the first detailed systems-level presentation of the methodology required for high-throughput neuroanatomy in a model primate.

neuroscience↗

bioRxiv2018

Continuity between koniocellular layers of dorsal lateral geniculate and inferior pulvinar nuclei in common marmosets

In primates, the koniocellular (K) layers of the dorsal lateral geniculate nucleus (LGN) and the calbindin-rich subdivisions of the inferior pulvinar (IPul) nucleus are considered part of a thalamic matrix system which projects diffusely to superficial cortical layers. Activity in the matrix system is proposed to coordinate oscillatory activity in thalamocortical loops. Further, since both K cells and IPul are involved in visual processing pathways, especially in alternative pathways to visual cortex after V1 lesion in early life (\"blindsight\"), their functional similarities have been strongly implicated. Here we tested the hypothesis that calbindin-positive K cells and IPul cells constitute a continuous group of cells. By combining immunohistochemistry and a high-throughput neuronal tracing method, we found that both K cells and IPul form reciprocal connections with striate and extrastriate cortices; whereas principal laminae of LGN do not receive inputs from extrastriate cortex and only project sparsely to these areas. Retrograde labelled cells in lateral division of IPul merged seamlessly into the retrograde labelled cells in K layers. These results supported the continuity between LGN K layers and IPul, providing the anatomical basis for functional congruity of this part of dorsal thalamic matrix.

neuroscience↗

bioRxiv2018

On variational solutions for whole brain serial-section histology using the computational anatomy random orbit model

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.\n\nAuthor SummaryNew developments in neural tracing techniques have motivated the widespread use of histology as a modality for exploring the circuitry of the brain. Automated mapping of pre-labeled atlases onto modern large datasets of histological imagery is a critical step for elucidating the brains neural circuitry and shape. This task is challenging as histological sections are imaged independently and the reconstruction of the unsectioned volume is nontrivial. Typically, neuroanatomists use reference volumes of the same subject (e.g. MRI) to guide reconstruction. However, obtaining reference imagery is often non-standard, as in high-throughput animal models like mouse histology. Others have proposed using anatomical atlases as guides, but have not accounted for the intrinsic nonlinear shape difference from atlas to subject. Our method addresses these limitations by jointly optimizing reconstruction informed by an atlas simultaneously with the nonlinear change of coordinates that encapsulates anatomical variation. This accounts for intrinsic shape differences and enables rigorous, direct comparisons of atlas and subject coordinates. Using simulations, we demonstrate that our method recovers the reconstruction parameters more accurately than atlas-free models and innately produces accurate segmentations from simultaneous atlas mapping. We also demonstrate our method on the Mouse Brain Architecture dataset, successfully mapping and reconstructing over 500 brains.

neuroscience↗