The Neuroterrain 3D Mouse Brain Atlas

A significant objective of neuroinformatics is the construction of tools to readily access, search, and analyze anatomical imagery. This goal can be subdivided into development of the necessary databases and of the computer vision tools for image analysis. When considering mesoscale images, the latter tools can be further divided into registration algorithms and anatomical models. The models are atlases that contain both bitmap images and templates of anatomical boundaries. We report here on construction of such a model for the C57BL/6J mouse. The intended purpose of this atlas is to aid in automated delineation of the Mouse Brain Library, a database of brain histological images of importance to neurogenetic research

Using an Internal Auditory Stimulus to Activate the Developing Primary Auditory Cortex: A Fetal fMRI Study

Insight into the rapidly developing brain in utero is scarce. Fetal functional magnetic resonance imaging (fMRI) is a technique used to gain awareness into the developmental process. Previous auditory task-based fMRI studies employed an external sound stimulus directly on the maternal abdomen. However, there has since been recommendation to cease doing so. We sought to investigate a reliable paradigm to study the development of fetal brain networks and postulate that by using an internal stimulus, such as the mother singing, it would result in activation of the fetal primary auditory cortex. Volunteers carrying singleton fetuses with a gestational age of 33-38 weeks underwent two stimulus-based block design BOLD fMRI series. All of the nine fetal subjects analyzed had activation in the right Heschl’s gyrus, and seven out of the nine fetal subjects had activation in the left Heschl’s gyrus when exposed to the internal acoustic stimulus. Ultimately, this internal auditory stimulus can be used to analyze the developing fetal brain

The Developing Human Connectome Project: a minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth