Quantitative study on the hygroscopic expansion of spurr resin to obtain a high-resolution atlas of the mouse brain

Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level

The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve "optical sectioning" by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.

Review of micro-optical sectioning tomography (MOST): technology and applications for whole-brain optical imaging [Invited]

Elucidating connectivity and functionality at the whole-brain level is one of the most challenging research goals in neuroscience. Various whole-brain optical imaging technologies with submicron lateral resolution have been developed to reveal the fine structures of brain-wide neural and vascular networks at the mesoscopic level. Among them, micro-optical sectioning tomography (MOST) is attracting increasing attention, as a variety of technological variations and solutions tailored toward different biological applications have been optimized. Here, we summarize the recent development of MOST technology in whole-brain imaging and anticipate future improvements.

Biophotonics in China

Biophotonics is a highly interdisciplinary field where physicists, chemists, biologists, physicians and engineers work together to solve the problems appearing in biology and medicine. In China, the Biophotonics discipline is often referred to as Biomedical Photonics, under the first-level disciplines Biomedical Engineering or Optical Engineering, and was initiated in the late 1990s. Over the past 20 years, biophotonics research in China expanded extraordinarily and has reached the frontiers of the world-level sciences. This white paper introduces the research groups in the biophotonics field in China, and their representative contributions.

Optical Brain Imaging: A Powerful Tool for Neuroscience

As the control center of organisms, the brain remains little understood due to its complexity. Taking advantage of imaging methods, scientists have found an accessible approach to unraveling the mystery of neuroscience. Among these methods, optical imaging techniques are widely used due to their high molecular specificity and single-molecule sensitivity. Here, we overview several optical imaging techniques in neuroscience of recent years, including brain clearing, the micro-optical sectioning tomography system, and deep tissue imaging.

Automated and accurate detection of soma location and surface morphology in large-scale 3D neuron images

Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.