A group of researchers from the Spanish National Research Council (CSIC) have harnessed Artificial Intelligence (AI) to to detect and analyse hippocampal ripples, which are patterns of brain activity that signal memory formation.
These hippocampal ripples are considered electroencephalographic (EEG) biomarkers of memory, epilepsy and Alzheimer’s disease.
They are a type of fast brain oscillation that underlie the organisation of memories. However, ripples exhibit various waveforms and properties that can be missed by standard spectral methods.
In this study, the team used recordings obtained in laboratory mice to train a toolbox of machine learning models, which emerged as a result of a hackathon.
This resulted in a short list for the best detection models, and th architectures were harmonised and optimised by the authors who now provide all codes and data openly to the research community.
De la Prida, who is part of the CSIC’s AI-HUB connection aimed at advancing the use of AI and its applications, stated: “We have tested the ability of these models using data from non-human primates that were collected at Vanderbilt University (Nashville, USA) by Saman Abbaspoor and lab leader Kari Hoffman as part of the Brain Initiative.
“We found that it is possible to use rodent EEG data to train AI algorithms that can be applied to data from primates and possibly human, provided the same type of recording techniques are used.”
Models include some of the best-known supervised learning architectures, such as support vector machines, decision trees, and convolutional neural networks.
“We have identified more than one hundred possible models from the different architectures that are now available for application or retraining by other researchers,” added Andrea Navas Olivé and Adrián Rubio, first authors of the work.
“This bank of AI models will provide new applications in the field of neurotechnologies and can be useful for detection and analysis of high-frequency oscillations in pathologies such as epilepsy, where they are considered clinical markers,” De la Prida concluded.
