Connectomics, the ambitious field of study that seeks to map the intricate network of animal brains, is experiencing accelerated growth. In the span of a decade, it has traveled from its nascent stages to becoming a discipline that is poised (hopefully) to unlock the enigmas of cognition and the physical basis of neuropathologies such as Alzheimer’s disease.
At the forefront is the use of powerful electron microscopes, which researchers at MIT’s Computer Science and artificial intelligence Laboratory (CSAIL) and Harvard University’s Samuel and Lichtman Laboratories have endowed with the analytical prowess of machine learning. Unlike traditional electron microscopy, embedded ai serves as a “brain” that learns a specimen as it acquires images and intelligently focuses on relevant pixels with nanoscale resolution similar to how animals survey their worlds.
“Smart EM”helps connectomics to rapidly examine and reconstruct the brain’s complex network of synapses and neurons with nanometer precision. Unlike traditional electron microscopy, its integrated ai opens new doors to understanding the intricate architecture of the brain.
The integration of hardware and software into the process is crucial. The team incorporated a GPU into the support computer connected to their microscope. This allowed machine learning models to be run on the images, which helped direct the microscope beam to areas deemed interesting by the ai. “This allows the microscope to stay longer in areas that are harder to understand until it captures what it needs,” says MIT professor and CSAIL principal investigator Nir Shavit. “This step helps mirror the control of the human eye, allowing for rapid understanding of images.”
“When we look at a human face, our eyes quickly navigate to focal points that provide vital cues for effective communication and understanding,” says SmartEM lead architect Yaron Meirovitch, a visiting scientist at MIT CSAIL and former postdoc. and current associate research neuroscientist at Harvard. “When we immerse ourselves in a book, we don’t scan all the empty space; rather, we direct our gaze toward words and characters with ambiguity in relation to our prayer expectations. “This phenomenon within the human visual system has paved the way for the birth of the novel microscope concept.”
For the task of reconstructing a human brain segment of approximately 100,000 neurons, accomplishing this with a conventional microscope would require a decade of continuous imaging and a prohibitive budget. However, with SmartEM, by investing in four of these innovative microscopes at less than $1 million each, the task could be completed in as little as three months.
Nobel Prizes and worms
More than a century ago, Spanish neuroscientist Santiago Ramón y Cajal was heralded as the first to characterize the structure of the nervous system. Employing the rudimentary optical microscopes of his time, he embarked on leading explorations in neuroscience, laying the foundations of knowledge of neurons and sketching the initial contours of this expansive and unexplored realm, a feat that earned him the Nobel Prize. He noted, on the themes of inspiration and discovery, that “As long as our brain is a mystery, the universe, the reflection of the brain’s structure, will also be a mystery.”
From these early stages, the field has advanced dramatically, as evidenced by efforts in the 1980s, which mapped the relatively simpler connectome of C. elegans, small worms, to current efforts investigating more complex brains of organisms such as zebrafish and mice. This evolution reflects not only enormous advances, but also increasing complexities and demands: mapping the mouse brain alone means managing a staggering number of thousands of petabytes of dataa task that vastly dwarfs the storage capabilities of any university, the team says.
Testing the waters
For their own work, Meirovitch and other members of the research team studied 30-nanometer-thick slices of octopus tissue that were mounted on ribbons, placed on wafers, and finally inserted into electron microscopes. Each section of an octopus brain, comprising billions of pixels, was imaged, allowing scientists to reconstruct the slices into a three-dimensional cube with nanometer resolution. This provided an ultra-detailed view of the synapses. The main objective? To color these images, identify each neuron and understand their interrelationships, thus creating a detailed map or “connectome” of the brain’s circuits.
“SmartEM will reduce imaging time for these types of projects from two weeks to a day and a half,” says Meirovitch. “Neuroscience laboratories that currently cannot work with expensive and lengthy EM imaging will now be able to do so.” The method should also allow the analysis of circuits at the synapse level in samples from patients with psychiatric and neurological disorders.
Going forward, the team envisions a future where connectomics is affordable and accessible. They hope that with tools like SmartEM, a broader spectrum of research institutions can contribute to neuroscience without relying on large partnerships, and that the method will soon become a standard avenue where biopsies from living patients are available. Additionally, they are eager to apply the technology to understand pathologies, expanding the utility beyond connectomics. “We are now trying to introduce this into hospitals for large biopsies, using electron microscopes, with the goal of making pathology studies more efficient,” says Shavit.
Two other authors of the paper have ties to MIT CSAIL: lead author Lu Mi MCS ’19, PhD ’22, who is now a postdoc at the Allen Institute for Brain Sciences, and Shashata Sawmya, a graduate student in the MIT lab. The other lead authors are Core Francisco Park and Pavel Potocek, while Harvard professors Jeff Lichtman and Aravi Samuel are additional lead authors. Their research was supported by the NIH BRAIN Initiative and was presented at the 2023 International Conference on Machine Learning (ICML) Computational Biology Workshop. The work was done in collaboration with scientists at Thermo Fisher Scientific.
