Brain activity captured in exceptional resolution
Researchers have developed a new technology for imaging neuron activity within the brain at high resolution, scale, and speed.
The technology could also dramatically lower the computational costs inherent to processing the terabytes of raw data generated when imaging the brain in high detail.
The cognition of animals and humans involves information flowing across networks of deeply interconnected brain cells. The scale of these networks has posed a major challenge for scientists looking to better understand the mechanics of cognition. This is because imaging tools have historically been incapable of tracing how neurons fire in sync from the far reaches of the cortex.
In a new study published in Nature Methods, scientists from Rockefeller University describe a mesoscopic technology that enables the breadth and depth of the brain to be imaged at peak resolution, scale and speed.
“The challenge with using mesoscopes for visualising the fast activity of single neurons in 3D is that high-resolution point-scanning approaches are typically needed, for which the scanning time scales very unfavourably with the size of the imaged volume,” remarked Professor Alipasha Vaziri, of the university’s Laboratory of Neurotechnology and Biophysics.
The new technology developed by Vaziri’s team, dubbed MesoLF, can capture key interactions between 10,500 neurons within a mouse brain at once – imaging cells buried at previously inaccessible depths, firing from brain regions many millimetres apart – and with unprecedented resolution.
Vaziri explained that the technology is a spin-off of light-field microscopy (LFM), a 3D imaging technique known for providing fast, high-resolution imaging. For all its strengths, however, LFM performs poorly deep inside scattering tissue such as the mouse brain, where dense tissue scatters light.
The researchers previously circumvented some of these limitations with a machine-learning algorithm, which estimates the locations of active neurons to better detect brain cell activity in dense tissue. The latest work expands that reach by adding software and hardware to scale up the system, allowing it to peer into tissues of various shapes and rigidities.
Crucially, it also keeps the computational costs inherent to processing terabytes of raw data as low as possible.
“This is made possible through a custom optical design for maintaining high optical imaging resolution over mesoscopic volumes, in combination with a set of algorithmic innovations that scale our modular computational pipeline’s capacity and capabilities accordingly,” explained Vaziri.
Given the relatively low cost barrier in optical hardware, Vaziri hopes to make the MesoLF technology widely available to scientists studying the inner workings of the brain. His designs are now available under an open-source licence.