TL;DR:
- Johns Hopkins scientists have developed an AI-based method to visualize and track changes in synaptic strength in live animals.
- The technique offers insights into how connections in human brains change with learning, aging, injury, and disease.
- Machine learning is employed to enhance the quality of imaging data and enable the detection and tracking of individual synapses.
- The experiments involved genetically altered mice with fluorescent glutamate receptors.
- Environmental changes were found to affect synaptic strength, with a bias toward strengthening connections.
- This breakthrough has potential implications for understanding brain function and investigating synaptic changes in disease and injury contexts.
Main AI News:
Cutting-edge artificial intelligence techniques have been developed by researchers at Johns Hopkins to visualize and monitor fluctuations in synaptic strength—the crucial connection points facilitating communication between nerve cells in the brain—in live animals. This pioneering methodology, outlined in Nature Methods, promises to deepen our comprehension of how these connections in human brains evolve with learning, aging, injury, and disease.
Professor Dwight Bergles, from the Department of Neuroscience at the Johns Hopkins University School of Medicine, elaborates on the significance of the new method, stating, “If you want to learn more about how an orchestra plays, you have to watch individual players over time, and this new method does that for synapses in the brains of living animals.” Collaborating with him on this groundbreaking study are Assistant Professors Adam Charles and Jeremias Sulam from the Department of Biomedical Engineering, as well as Richard Huganir, the Bloomberg Distinguished Professor at JHU and director of the Neuroscience department. All four researchers are esteemed members of Johns Hopkins’ prestigious Kavli Neuroscience Discovery Institute.
Synapses, or junctions, serve as conduits for the exchange of chemical messages between nerve cells, transmitting vital information from one cell to another. Within the brain, it is widely believed that different life experiences, such as exposure to novel environments and the acquisition of new skills, induce changes in synapses, fortifying or weakening these connections to enable learning and memory formation. Investigating the intricate dynamics occurring across the trillions of synapses in our brains poses a formidable challenge. However, unraveling how the brain functions in a healthy state and how it is influenced by disease hinges upon our understanding of these minute changes.
Scientists have long sought improved visualization techniques to discern which synapses undergo modifications during specific life events. The dense arrangement and small size of synapses in the brain make them exceedingly difficult to observe, even with cutting-edge microscopes. “We needed to go from challenging, blurry, noisy imaging data to extract the signal portions we need to see,” explains Charles.
To surmount these obstacles, Bergles, Sulam, Charles, Huganir, and their esteemed colleagues turned to machine learning, a computational framework facilitating the development of flexible automatic data processing tools. Machine learning has already proven its efficacy across various domains within biomedical imaging. In this particular study, the scientists harnessed this approach to enhance the quality of images encompassing thousands of synapses. While machine learning offers exceptional speed, far surpassing human capabilities, the system must be trained first, teaching the algorithm what high-quality synapse images should look like.
In their experiments, the researchers worked with genetically modified mice in which glutamate receptors—chemical sensors at synapses—illuminated in a vibrant green hue when exposed to light. The fluorescence emitted by each receptor is indicative of the number of synapses and, thus, their strength. As expected, imaging the intact brain resulted in low-quality pictures where individual clusters of glutamate receptors were challenging to discern, let alone detect and track over time.
To enhance these images, the scientists employed a machine learning algorithm, training it using images obtained from brain slices (ex vivo) derived from the same type of genetically modified mice. This allowed them to produce higher-quality images using a different microscopy technique, as well as low-quality images—resembling those captured in live animals—of the same perspectives.
This cross-modality data collection framework empowered the team to develop an enhancement algorithm capable of generating higher-resolution images from lower-quality ones, akin to those obtained from living mice. Consequently, data acquired from the intact brain can be significantly enhanced, enabling the detection and tracking of individual synapses (in the thousands) during multiday experiments.
To examine changes in receptors over time in live mice, the researchers employed microscopy to capture repeated images of the same synapses over several weeks. After capturing baseline images, the animals were placed in a chamber enriched with novel sights, smells, and tactile stimuli for a brief five-minute period. Subsequently, the researchers imaged the same brain area every other day to investigate the effects of the new stimuli on the number of glutamate receptors at synapses.
While the primary focus of this study revolved around developing methods to analyze synaptic changes in various contexts, the researchers observed that this simple environmental alteration caused a spectrum of fluorescence alterations across synapses in the cerebral cortex. This indicates an increase in strength in certain connections and a decrease in others, with a bias towards strengthening in animals exposed to the novel environment.
These studies were made possible through close collaboration among scientists with diverse areas of expertise, spanning from molecular biology to artificial intelligence. Despite not typically working together closely, the researchers successfully leveraged machine learning to study synaptic changes in animal models of Alzheimer’s disease. They believe that this methodology could shed new light on synaptic alterations occurring in other disease and injury contexts.
Assistant Professor Jeremias Sulam expresses excitement about the broader scientific community’s potential utilization of their findings, stating, “We are really excited to see how and where the rest of the scientific community will take this.” The groundbreaking fusion of machine learning and neuroscience has opened up a wealth of possibilities for further exploration into the brain’s intricate adaptations and responses to various environments.
Conclusion:
The application of artificial intelligence and machine learning techniques in neuroscience research, as demonstrated by the work of Johns Hopkins scientists, opens up new avenues for understanding the dynamic nature of the brain. By visualizing and tracking synaptic changes in live animals, researchers gain valuable insights into the impact of learning, aging, injury, and disease on brain connectivity.
This breakthrough has significant implications for the market, as it paves the way for advancements in diagnostic tools, therapeutic interventions, and drug development targeting neurological disorders. The integration of machine learning algorithms with neuroscience research has the potential to revolutionize our understanding of the brain and its complexities, leading to improved treatments and better outcomes for patients in the future.
