In a leap forward for neuroscience imaging, MIT researchers have created an image sensor that can more accurately and vividly capture the electrical activities of neurons. This new tool can help to demystify how neurons communicate and could illuminate the intricate processes behind memory and learning. According to a paper published in Nature Communications, the research team, led by MIT postdoc Jie Zhang and professor Matt Wilson of The Picower Institute for Learning and Memory, has developed a novel sensor that has pixels capable of being programmed individually to optimize the signal-to-noise ratio for better visualizations of neural activity.
The standard CMOS (complementary metal-oxide semiconductor) image sensors, which are commonly used in scientific imaging, have a fixed turning on and off mechanism for pixels that often miss the mark in capturing the fleeting electrical exchanges of neurons. By allowing each pixel on the new sensor to operate on its own timeframe, adjacent pixels complement one another to capture all the light without forgoing speed. In a project partially funded by both private foundations and federal institutions, this breakthrough promises to enrich our understanding of neural function by shedding light on the rapid, even minute, changes in voltage that occur within the brain's neural network, according to MIT News.
"Measuring with single-spike resolution is really important as part of our research approach," Matt Wilson, a brain and cognitive sciences professor at MIT said in a statement obtained by MIT News. He detailed the importance of capturing the precise timing of neural spikes, which are critical for understanding how the brain processes information. With the new pixelwise sensor chip, researchers can track both the obvious and the subtle neural signals, revealing potentially groundbreaking insights into neuron-specific dynamics in memory and learning processes.
Traditional CMOS sensors often falter, either too slow to catch rapid signaling or too swift, losing out on detail in low light. This new chip's architecture features ingeniously engineered control electronics that minimize encroachment on light-sensitive elements, allowing for high sensitivity in dim conditions. In head-to-head comparisons with an industry-standard CMOS image sensor, Zhang’s design effectively doubled the signal-to-noise ratio and detected neural spikes that were previously missed. "We are already working on the next iteration of chips with lower noise, higher pixel counts, time-resolution of multiple kHz, and small form factors for imaging in freely behaving animals," Zhang remarked in an interview with MIT News, showcasing the forward momentum of their research endeavors.
The research, while still in the initial stages using clusters of neurons in a culture dish, aims to eventually conduct real-time, brain-wide measurements of neuron activity in freely moving animals. This could enable a deeper exploration of the spatial memories formed during periods of activity and quietude alike. The combination of developments in genetical encodable voltage indicators, which make cells light up to signal voltage changes, along with this newly refined image sensor provides a considerable leap towards visualizing the electrical symphony that orchestrates thought, memory, and behavior.
