Researchers at the University of Tsukuba have achieved a major breakthrough in brain imaging technology by adapting scatterometry, a technique originally used in semiconductor manufacturing, for the detailed 3D analysis of neurons. By combining this approach with machine learning, the team has increased measurement accuracy and speed by more than tenfold compared to conventional optical microscopes. Their findings were published in Optics Express.
The human brain contains billions of neurons that process and transmit information, yet many of their precise functions remain poorly understood. A particular challenge lies in unraveling how neurons encode and store memories. To study these mechanisms, scientists require noninvasive imaging methods capable of capturing neuronal structure and activity in fine detail.
A new level of precision
Traditional fluorescence microscopy, though widely used, has key drawbacks such as the need for chemical labeling, dye toxicity, and long imaging times. The Tsukuba team’s scatterometry-based approach avoids these limitations by providing noncontact, label-free, and nondestructive 3D imaging.
By analyzing the diffraction patterns of light projected onto cells, the system reconstructs neuronal shapes at unprecedented precision. Unlike lens-based microscopes that introduce imaging artifacts, this method achieves 0.2-micrometer accuracy for cells only 2 micrometers in diameter.
In principle, the technique can also detect vesicle positions within cells and capture electrical activity of neurons within a one-millisecond timeframe. This is a level of temporal resolution that was previously unattainable with optical systems.
Expanding scatterometry beyond semiconductors
Scatterometry has traditionally been limited to examining periodic microstructures, such as those found in chips. The research team overcame this limitation by developing new computational and analytical frameworks tailored to the irregular geometries of neurons.
Because neurons have relatively well-defined and repeatable shapes, this innovation makes scatterometry an especially promising tool for neuroscience research. The researchers believe the method could help unlock the structural and functional principles underlying memory formation and cognitive processing, paving the way for more advanced diagnostic and therapeutic technologies in neurology and mental health.
3D Brain model
Earlier this year, researchers at POSTECH in South Korea have developed an innovative 3D-printed brain model, known as the Bioengineered Neural Network (BENN), that could revolutionize the study and early detection of neurodegenerative diseases such as Alzheimer’s and Parkinson’s. Unlike traditional 2D cell cultures or organoids, BENN replicates both the structure and function of the human brain, featuring realistic grey and white matter organization and functional neural pathways formed through electrical stimulation.
To test its clinical relevance, the team exposed the model to moderate alcohol levels over three weeks, observing increased amyloid beta and tau proteins, nerve fibre deformation, and weakened neuronal signalling. These are all hallmarks of early Alzheimer’s pathology.
According to the researchers, BENN allows real-time, high-resolution monitoring of brain activity, providing a powerful tool for studying region-specific neurotoxicity. The technology holds great promise for personalised therapies, drug testing, and advancing precision medicine in neuroscience research.
