New AI Tracks Neurons in Moving Animals
Published in Brain/Neurology.
A groundbreaking AI method may accelerate cognitive neuroscience research.
Scientific research in complex fields such as neuroscience is getting a boost from artificial intelligence (AI) machine learning. A new study by researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Lausanne, Switzerland, and Harvard University in Cambridge, Massachusetts, U.S., shows how AI has the potential to advance neuroscience by identifying and tracking neurons in moving animals.
“Machine learning is ideally suited to automate the task of segmenting and tracking neurons,” wrote lead author Sahand Jamal Rahi, along with coauthors Aravinthan Samuel, Corinne Jones, Vladislav Susoy, Ariane Delrocq, Kseniia Korchagina, Mahsa Barzegar-Keshteli, and Core Francisco Park.
Cognitive neuroscience is the branch of neuroscience and biological psychology that studies the neural mechanisms of cognition. Brain imaging techniques such as functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), magnetoencephalography (MEG), optical imaging with near-infrared spectroscopy (NIRS), and positron emission tomography (PET) are used to study the human brain.
These imaging techniques generate complex, high-dimensional datasets that require segmentation and tracking of the pixels for each neuron. Surprisingly, this often requires painstaking and time-consuming manual annotation. In artificial intelligence, annotation refers to labeling data within datasets to be used by machine learning algorithms. The data elements may be in the form of text, images, videos, or voice data.