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Harvard Medical School researchers have shown that a new X-ray imaging technique could help construct a comprehensive map of the circuitry of the brain.
The labs of Wei-Chung Allen Lee, assistant professor of neurology at HMS, and Alexandra Pacureanu, a co-author from the European Synchrotron Radiation Facility, conducted the research, which was published Monday in the journal Nature Neuroscience.
Scientists currently use light microscopes and electron microscopes to visualize the circuitry of the brain. But these techniques have limitations, according to Jasper S. Phelps, a first author on the paper and graduate student in the Lee lab.
Visible light microscopy cannot clearly identify individual neurons, while electron microscopy has higher resolution but can only see a few neurons at a time. The HMS researchers’ new technique — called X-ray holographic nano-tomography, or XNH — can do both.
“XNH hits the sweet spot where it can image large enough pieces of brain to capture whole circuits of neurons, but while still seeing all neurons, and with enough resolution to trace most of their branches,” Phelps wrote in an email.
In the XNH technique, researchers shine X-rays through a rotating piece of neural tissue and observe phase changes as the X-ray waves pass through the sample.
Lee emphasized the difficulty of understanding the “incredible computational machine” that is the human brain, given the sheer number of neurons and synapses it contains.
“The human brain has about 86 billion neurons, and each of those neurons has 1000 connections, and so that's a network with like 100 trillion connections,” Lee said. “So trying to understand such a complex network is difficult if you don't have a decent roadmap, or in our case what we'd want is something called a wiring diagram, of how these neurons are connected together into these complex networks.”
Echoing Lee, Phelps wrote that the dense mass of neurons in the brain “looks like a ball of spaghetti that extends for miles and miles.”
Using the XNH technique, the researchers generated high-resolution, cross-sectional images of neural tissue from fruit flies and mice. They then used artificial intelligence algorithms to trace individual neurons from these images.
Despite the success of this approach, Aaron T. Kuan ’09 — a first author on the paper and postdoctoral fellow — acknowledged that XNH has some drawbacks. For instance, the technique cannot be applied to live tissue.
Also, while the researchers could identify individual neurons, they were unable to achieve high enough resolution to visualize the synapses between neurons in postmortem tissue. Kuan, however, believes that “further advancements in the technology” could solve this problem.
“What's really exciting to me is how applications in neuroscience can help drive further advances in X-ray imaging,” Kuan added. “I think this synergy can get us to a point where we can realistically generate synapse-level circuit maps of the whole brain.”
—Staff writer Meera S. Nair can be reached at email@example.com.
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