Journal of Visualized Experiments www.jove.com Copyright © 2019 Journal of Visualized Experiments May 2019 | 147 | e58911 | Page 1 of 9 Video Article 3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol Saya Ide 1 , Motoki Kajiwara 1 , Hirohiko Imai 2 , Masanori Shimono 1 1 Graduate School of Medicine and Faculty of Medicine, Kyoto University 2 Graduate School of Informatics, Kyoto University Correspondence to: Masanori Shimono at shimono.masanori.7w@kyoto-u.ac.jp URL: https://www.jove.com/video/58911 DOI: doi:10.3791/58911 Keywords: Neuroscience, Issue 147, Brain, neural networks, magnetic resonance imaging (MRI), three-dimensional (3D) scan, mapping, scale, connectomics Date Published: 5/12/2019 Citation: Ide, S., Kajiwara, M., Imai, H., Shimono, M. 3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol. J. Vis. Exp. (147), e58911, doi:10.3791/58911 (2019). Abstract The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and microscopic neuronal spikes, propagating along axons and dendrites. Both scales complement different aspects of human cognitive and behavioral functions. At the macroscopic level, MRI has been the current standard imaging technology, in which the smallest spatial resolution, voxel size, is 0.1–1 mm 3 . Also, at the microscopic level, previous physiological studies were aware of nonuniform neuronal architectures within such voxels. This study develops a powerful way to accurately embed microscopic data into a macroscopic map by interfacing biological scientific research with technological advancements in 3D scanning technology. Since 3D scanning technology has mostly been used for engineering and industrial design until now, it is repurposed for the first time to embed microconnectomes into the whole brain while preserving natural spiking in living brain cells. In order to achieve this purpose, first, we constructed a scanning protocol to obtain accurate 3D images from living bio-organisms inherently challenging to image due to moist and reflective surfaces. Second, we trained to keep speed to prevent the degradation of living brain tissue, which is a key factor in retaining better conditions and recording more natural neuronal spikes from active neurons in the brain tissue. Two cortical surface images, independently extracted from two different imaging modules, namely MRI and 3D scanner surface images, surprisingly show a distance error of only 50 μm as mode value of the histogram. This accuracy is comparable in scale to the microscopic resolution of intercellular distances; also, it is stable among different individual mice. This new protocol, the 3D novel embedding overlapping (3D-NEO) protocol, bridges macroscopic and microscopic levels derived by this integrative protocol and accelerates new scientific findings to study comprehensive connectivity architectures (i.e., microconnectome). Video Link The video component of this article can be found at https://www.jove.com/video/58911/ Introduction Nonuniform multiscale architectures at various physical and biological organizations are commonly found 1,2 . The brain is also a very nonuniform and multiscale network organization 3,4 . Various cognitive functions are coded in such network organizations, holding temporal changes of electrical spike patterns of neuronal populations in submillisecond temporal resolutions. Historically, the complex networks among neurons were structurally observed in detail using the staining techniques by Santiago Ramón y Cajal from over 150 years ago 5 . To observe group behaviors of active neurons, researchers have developed various recording technologies 6,7,8 , and the recent significant developments of such technologies have enabled us to record electrical activities from huge numbers of neurons simultaneously. Furthermore, from such functional activities, scientists have succeeded to reconstruct networks of causal interactions among huge numbers of neurons and have declared the topological architecture of their complex interactions ‘microconnectome’ 9 . Macroscopic observations of the brain also allow for regarding a whole brain as a network organization because many brain regions are connected by multiple fiber-bundles. The embedding of microconnectomes into the global brain map still has clear limitations within current technological advances, which is why this embedding protocol is so important. However, there are many challenges to the development of the embedding protocol. For example, in order to observe activities of living local neuronal circuits in purely isolated brain regions, brain slices need to be produced for in vitro recordings. Additionally, recordings from brain slices for in vitro recordings are still an important choice for at least two reasons. First, it is still not easy to observe activities of many living individual neurons simultaneously from brain regions deeper than ~1.5 mm and in high temporal resolution (<1 ms). Second, when we hope to know the internal architecture of a local neuronal circuit, we need to stop all inputs coming from external brain regions to eliminate confounding factors. In order to identify the directions and positions of produced brain slices, it will be further necessary to integrate the spatial positions of these produced brain slices using coordinates. There are, however, a few systematic and reliable ways to make brain slices in an organized way 10,11 . Here, a new coregistration protocol is introduced, using 3D scanning technology for neuroscientific research in order to provide an integrative protocol. This protocol acts to coordinate micro- and macroscales and embed multielectrode array (MEA) microdata 12,13 and staining data onto a macroscopic MRI space through 3D scan surfaces of extracted brains, as well as of noninvasively recorded brains. Surprisingly, this showed a distance error of only ~50 μm as the mode value of the histogram. As a result, the mode values of minimum distances between two surfaces between the