I will praise Thee; for I am fearfully and wonderfully made:
Psalm 139:14
"The brain is the most complex organ ever created. 
Its functions are supported by a network of tens of billions of densely packed neurons, with trillions of connections exchanging information and performing calculations. Trying to understand the complexity of the brain can be dizzying. Nevertheless, if we hope to understand how the brain works, we need to be able to map neurons and study how they are wired.
"One of the biggest challenges in neuroscience is trying to map the brain and its connections. However, because neurons are so densely packed, it's very difficult and time-consuming to distinguish neurons with their axons and dendrites—the extensions that send and receive information from other neurons—from each other," explains Professor Takeshi Imai of the Graduate School of Medical Sciences, who led the study.
QDyeFinder works by first automatically identifying fragments of axons and dendrites in a given sample. It then identifies the color information of each fragment. Then, utilizing a machine-learning-algorithm the team developed called dCrawler, the color information was grouped together, wherein it would identify axons and dendrites of the same neuron.
Firstly, we establish stochastic super-multicolor labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing.
We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations.
To classify color combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance.
"There may come a day when we can read the connections in the brain and understand what they mean or represent for that person. I doubt it will happen in my lifetime, but our work represents a tangible step forward in understanding perhaps the most complicated and mysterious dimension of our existence," concludes Imai." MedicalXpress
"One of the biggest challenges in neuroscience is trying to map the brain and its connections. However, because neurons are so densely packed, it's very difficult and time-consuming to distinguish neurons with their axons and dendrites—the extensions that send and receive information from other neurons—from each other," explains Professor Takeshi Imai of the Graduate School of Medical Sciences, who led the study.
Firstly, we establish stochastic super-multicolor labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing.
We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations.
To classify color combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance.
"There may come a day when we can read the connections in the brain and understand what they mean or represent for that person. I doubt it will happen in my lifetime, but our work represents a tangible step forward in understanding perhaps the most complicated and mysterious dimension of our existence," concludes Imai." MedicalXpress
