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A new breakthrough in neuroscience, published in Nature Methods, offers a powerful tool for understanding the brain’s inner workings. Researchers from the Signal Processing Laboratory LTS2 at the École Polytechnique Fédérale de Lausanne (EPFL), led by Pierre Vandergheynst, have developed a novel geometric deep learning approach called MARBLE (Manifold Representation Basis Learning). This method could revolutionize how scientists decode complex brain activity and understand the neural patterns behind behavior.
In the classic parable of the blind men and the elephant, different individuals describe the same animal based on their limited perspectives, highlighting the challenge of understanding a larger reality from incomplete information. Similarly, studying brain dynamics often involves piecing together fragmented recordings from a small number of neurons, making it difficult to infer the broader patterns that drive behavior. Vandergheynst and his team sought to address this challenge by developing MARBLE, which can identify latent brain activity patterns across different experimental subjects.
The research, conducted by Vandergheynst and former postdoctoral fellow Adam Gosztolai, now an assistant professor at the AI Institute of the Medical University of Vienna, demonstrates how MARBLE can reveal the brain’s dynamic motifs—key patterns of neural activity that underpin mental tasks. By breaking down complex neural signals, MARBLE uses a geometric neural network to learn these dynamic motifs, which are independent of the shape of the underlying neural activity space. This allows the tool to provide interpretable and accurate representations of brain activity from multiple animals engaged in the same task, even when the subjects are different species, such as macaques and rats.
The team applied MARBLE to neural recordings from the pre-motor cortex of macaques during a reaching task and the hippocampus of rats during spatial navigation. The results were striking—MARBLE was able to decode brain activity related to arm movements and navigation with greater precision than traditional machine learning methods. Its ability to work within high-dimensional, curved spaces means it can identify consistent brain motifs across different recording conditions, without requiring a predefined global shape.
Beyond its potential in neuroscience, MARBLE holds promise for various applications. By recognizing dynamic brain patterns during specific tasks, it could help trigger assistive devices like robotic arms or prosthetics, providing a practical application for brain-machine interfaces. Moreover, the tool’s underlying mathematical framework makes it suitable for a wide range of scientific fields, from life sciences to physics, enabling researchers to analyze complex dynamic data across diverse datasets.
“The MARBLE method is primarily aimed at helping neuroscience researchers understand how the brain computes across individuals or experimental conditions, and to uncover—when they exist—universal patterns,” Vandergheynst explains. “However, its mathematical basis is by no means limited to brain signals. We expect this tool to benefit researchers in other fields of life and physical sciences who wish to jointly analyze multiple datasets.”
This exciting development could lead to a deeper understanding of how the brain works, ultimately leading to better treatments for neurological conditions and advancements in human-computer interaction.
Disclaimer: The content and conclusions presented in this article are based on the study published by Adam Gosztolai et al. in Nature Methods. The application of MARBLE to brain-machine interfaces and other fields is still in its early stages, and further research is required to fully realize its potential across various scientific domains.
