Researchers captured the complete neural activity of the C. elegans worm.
The reconstructed image of brain activity in a zebrafish larvae head. Image: Research Institute of Molecular Pathology
In a step forward for brain imaging, scientists at MIT and Austria’s University of Vienna have discovered a way of generating “3D movies” of living animals' brains in their entirety.
It might just look like flashing lights, but it’s a step to understanding how brains actually work—something that still evades us to a large extent. This video shows the full neural workings of a worm called C. elegans, with neural activity throughout its entire body captured simultaneously:
Being able to image the whole brain and nervous system at once is a big step, because we know that cognitive function doesn't rely on the activity of individual neurons but the interaction between them, so just studying one area or a few neurons doesn't give the full picture. "It is as if you would be trying to infer a movie on the TV just by looking at a few pixels," researcher Alipasha Vaziri from the University of Vienna told me.
In a paper published in Nature Methods, the team explained that whole brains have been imaged before, but that previous techniques have been either very slow to execute, or limited in terms of resolution and size. They turned to light-field microscopy—a technique that has in the past fallen into that second category—and gave it a boost to make up for those trade offs.
Thanks to an array of microlenses, each point of light picked up by the microscope is refracted into different points, which a computer algorithm then uses to figure out its 3D placement. The authors wrote that this allowed for single neuron resolution and millisecond-fast imaging, with a field of view big enough to capture the entire worm rather than just its head region.
Screenshot of the C. worm's neuron activity
The C. elegans worm is an interesting subject of study when it comes to the brain—and one we’ve seen used before in brain-mapping and brain simulation efforts—because it only has 302 neurons (compare that to the tens of billions in a human brain). That makes it a natural place to start, but the researchers also tried their method on zebrafish larvae, which is often used as an animal model for neurological research and whose brains are much closer to ours in terms of speed and scope. There, they imaged 5,000 activated neurons of the zebrafish’s total 100,000.
The researchers also expect other scientists to adopt the imaging methods, for example to study behaviour. “LFDM [“light field deconvolution microscope” imaging] is easy to set-up, cost-effective and compatible with standard microscopes,” they wrote. “In addition both the temporal resolution and the size of the field of views that can be imaged, make LFDM an attractive technique for future combination with behavioral studies, where representation and processing of information by an entire nervous system, from sensory input neurons to motor neurons could be investigated.”
There’s also the potential for exploring brain disorders, as being able to look at activity in the whole nervous system at once could help highlight which neurons and networks are involved when things go wrong.
Vaziri explained that using the imaging technique on human brains would be difficult as you need optical access: The worm and zebrafish are relatively transparent, but human skulls definitely aren't. The team is however hoping to develop the technique to use it on animal models that are closer to humans, like mice and rats.
They also want to combine the method with optogenetics, which allows you not just to observe but to control neurons using light. "Ultimately what you will be aiming at is to have a full optical interrogation system of entire brains," said Vaziri. "Then you can go from correlations to causation relationships." The long term vision is to try to reverse-engineer the brain and reveal the computational algorithms it uses to complete tasks. As well as the incredible biological insight that would give, it could also inform efficient computing mechanisms.
But the first step to understanding the brain is observing it, and with this method, we just got a new vantage point.