Connectomics, the ambitious field of study that seeks to map the intricate network of animal brains, is undergoing a growth spurt. Inside the span of a decade, it has journeyed from its nascent stages to a discipline that’s poised to (hopefully) unlock the enigmas of cognition and the physical underpinning of neuropathologies corresponding to in Alzheimer’s disease. 

At its forefront is the usage of powerful electron microscopes, which researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Samuel and Lichtman Labs of Harvard University bestowed with the analytical prowess of machine learning. Unlike traditional electron microscopy, the integrated AI serves as a “brain” that learns a specimen while acquiring the photographs, and intelligently focuses on the relevant pixels at nanoscale resolution just like how animals inspect their worlds. 

“SmartEM” assists connectomics in quickly examining and reconstructing the brain’s complex network of synapses and neurons with nanometer precision. Unlike traditional electron microscopy, its integrated AI opens latest doors to grasp the brain’s intricate architecture.

The mixing of hardware and software in the method is crucial. The team embedded a GPU into the support computer connected to their microscope. This enabled running machine-learning models on the photographs, helping the microscope beam be directed to areas deemed interesting by the AI. “This lets the microscope dwell longer in areas which can be harder to grasp until it captures what it needs,” says MIT professor and CSAIL principal investigator Nir Shavit. “This step helps in mirroring human eye control, enabling rapid understanding of the photographs.” 

“Once we take a look at a human face, our eyes swiftly navigate to the focal points that deliver vital cues for effective communication and comprehension,” says the lead architect of SmartEM, Yaron Meirovitch, a visiting scientist at MIT CSAIL who can be a former postdoc and current research associate neuroscientist at Harvard. “Once we immerse ourselves in a book, we do not scan the entire empty space; relatively, we direct our gaze towards the words and characters with ambiguity relative to our sentence expectations. This phenomenon inside the human visual system has paved the best way for the birth of the novel microscope concept.” 

For the duty of reconstructing a human brain segment of about 100,000 neurons, achieving this with a standard microscope would necessitate a decade of continuous imaging and a prohibitive budget. Nonetheless, with SmartEM, by investing in 4 of those revolutionary microscopes at lower than $1 million each, the duty could possibly be accomplished in a mere three months.

Nobel Prizes and little worms  

Over a century ago, Spanish neuroscientist Santiago Ramón y Cajal was heralded as being the primary to characterize the structure of the nervous system. Employing the rudimentary light microscopes of his time, he launched into leading explorations into neuroscience, laying the foundational understanding of neurons and sketching the initial outlines of this expansive and uncharted realm — a feat that earned him a Nobel Prize. He noted, on the topics of inspiration and discovery, that “So long as our brain is a mystery, the universe, the reflection of the structure of the brain can even be a mystery.”

Progressing from these early stages, the sphere has advanced dramatically, evidenced by efforts within the Nineteen Eighties, mapping the relatively simpler connectome of C. elegans, small worms, to today’s endeavors probing into more intricate brains of organisms like zebrafish and mice. This evolution reflects not only enormous strides, but additionally escalating complexities and demands: mapping the mouse brain alone means managing a staggering thousand petabytes of data, a task that vastly eclipses the storage capabilities of any university, the team says. 

Testing the waters

For their very own work, Meirovitch and others from the research team studied 30-nanometer thick slices of octopus tissue that were mounted on tapes, placed on wafers, and eventually inserted into the electron microscopes. Each section of an octopus brain, comprising billions of pixels, was imaged, letting the scientists reconstruct the slices right into a three-dimensional cube at nanometer resolution. This provided an ultra-detailed view of synapses. The chief aim? To colorize these images, discover each neuron, and understand their interrelationships, thereby creating an in depth map or “connectome” of the brain’s circuitry.

“SmartEM will cut the imaging time of such projects from two weeks to 1.5 days,” says Meirovitch. “Neuroscience labs that currently cannot be engaged with expensive and long EM imaging will have the ability to do it now,” The strategy must also allow synapse-level circuit evaluation in samples from patients with psychiatric and neurologic disorders. 

Down the road, the team envisions a future where connectomics is each inexpensive and accessible. They hope that with tools like SmartEM, a wider spectrum of research institutions could contribute to neuroscience without counting on large partnerships, and that the strategy will soon be an ordinary pipeline in cases where biopsies from living patients can be found. Moreover, they’re desperate to apply the tech to grasp pathologies, extending utility beyond just connectomics. “We at the moment are endeavoring to introduce this to hospitals for giant biopsies, utilizing electron microscopes, aiming to make pathology studies more efficient,” says Shavit. 

Two other authors on the paper have MIT CSAIL ties: lead creator Lu Mi MCS ’19, PhD ’22, who’s now a postdoc on the Allen Institute for Brain Science, and Shashata Sawmya, an MIT graduate student within the lab. The opposite lead authors are Core Francisco Park and Pavel Potocek, while Harvard professors Jeff Lichtman and Aravi Samuel are additional senior authors. Their research was supported by the NIH BRAIN Initiative and was presented on the 2023 International Conference on Machine Learning (ICML) Workshop on Computational Biology. The work was done in collaboration with scientists from Thermo Fisher Scientific.