Connectomics Imaging
Connectomics Imaging - Related Videos
09:40
A Scanning Electron Microscopy-Compatible Optical Imaging Method for Mesoscopic All-Cell Brain Mapping
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2026
We introduced a novel imaging technique called Optical Multilayer Interference Tomography (OMLIT), which enables unbiased imaging of all cells in brain specimens at the mesoscale and can be seamlessly integrated into the imaging workflow of tape-based serial scanning electron microscopy on the same sample.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
09:08
Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
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Cited by 59 •
2011
Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.
06:46
Non-invasive In Vivo Fluorescence Optical Imaging of Inflammatory MMP Activity Using an Activatable Fluorescent Imaging Agent
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Cited by 4 •
2017
This paper explains the application of fluorescent imaging using an activatable optical imaging probe to visualize the in vivo activity of key matrix metalloproteinases in two different experimental models of inflammation.
09:57
Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors
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Cited by 5 •
2016
A method for imaging changes in membrane potential using genetically encoded voltage indicators is described.
FAQs
Neuronal labeling marks selected cells or cellular structures so they can be distinguished within brain tissue. High-resolution microscopy then captures detailed images of those labeled elements, including axons, dendrites, and synapses. Together, these components provide the visual information needed to follow neural structures across tissue and preserve the anatomical detail required for circuit-level analysis.
Computational reconstruction converts complex image data into organized representations of neural pathways and connections. It helps trace cellular features across multiple image regions, assemble those traces into circuit maps, and relate individual structures to larger network organization. This computational step makes extensive connectivity patterns interpretable rather than leaving them as disconnected microscopy observations.
Structural maps show how neural elements are arranged and connected, whereas functional measurements indicate how those networks operate. Combining both types of information helps researchers examine whether connectivity patterns correspond to neural activity and behavior. This relationship is important for studying how circuit organization contributes to sensory processing, learning, memory, and neurological disease.
A typical workflow begins with neuronal labeling and preparation of brain tissue for high-resolution image acquisition. Microscopy produces detailed images containing cellular features, after which computational reconstruction traces axons, dendrites, synapses, and related structures. Researchers can then organize these traces into connectivity maps and compare them with functional or behavioral information when available.
Researchers apply this approach when they need to examine how circuit organization relates to processes such as sensory processing, learning, or memory. The resulting maps provide a structural framework for interpreting neural systems and can also support investigations of neurological disease. Its value is greatest when cellular connectivity must be considered alongside activity or behavior.
Connectomics Imaging provides a common structural framework for examining networks under different biological conditions. Researchers can compare the organization of axons, dendrites, synapses, and other cellular features between healthy and diseased tissue. These comparisons may clarify how altered connectivity relates to changes in neural activity and help place disease-related circuit differences within a broader neuroscience context.