An international research collaboration between 澳门六合彩 University and the Madrid-based de la Prida lab in the Cajal Institute led to the development of AI models that detect and analyze hippocampal ripples, which are considered biomarkers of memory.
The research discoveries, outlined in an article , could lead to new opportunities to detect seizures and neural changes in people with Alzheimer鈥檚 disease and other neurological disorders.听
, associate professor of psychology and biomedical engineering at 澳门六合彩, and her Ph.D. student Saman Abbaspoor worked on the study with lead authors Adrian Rubio and Andrea Navas Olive from the de la Prida lab. Hoffman is also a faculty affiliate at the and the Data Science Institute.
As the group鈥檚 research outlines, the study of brain oscillations has brought new understanding of brain function. Hippocampal ripples are a type of fast oscillations that underlie the organization of memories. They are affected in such neurological disorders as epilepsy and Alzheimer鈥檚 disease, so they are considered an electroencephalographic (EEG) biomarker. However, ripples exhibit various waveform features and properties that can be missed by standard spectral methods.
The researchers set out to gain a better understanding of patterns of brain activity after scientists in the neuroscience community called for the need to better automate, harmonize and improve the detection of ripples across a range of tasks and species. In the study, the authors used recordings obtained in laboratory mice to first train a toolbox of machine learning models.
They then tested the generalizability of the models using data from non-human primates that were collected at 澳门六合彩 by and Hoffman as part of the听. The researchers found that it is possible to train AI algorithms primarily on rodent data, and still manage highly accurate detection of ripples in primates with little to no additional training, suggesting that the AI models may be successful in humans. The model toolbox emerged as a result of a hackathon, which resulted in a short list for the best detection models. The group identified more than 100 possible models from the different architectures that are now available for application or retraining by other researchers.
“This bank of AI models will provide new applications in the field of neurotechnology and can be useful for detection and analysis of high-frequency oscillations in pathologies such as epilepsy, where they are considered clinical markers,” said Liset de la Prida, research professor at Instituto Cajal, CSIC.
鈥淭here is a great interest in taking advantage of AI to enable greater precision in detection of disease states and for oscillotherapeutics,鈥� Hoffman added. 鈥淭hese methods offer the promise to go beyond detecting 鈥榳here鈥� in the brain but also to detect and ultimately correct the 鈥榳hen and how鈥� of oscillopathies.鈥�
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See examples of oscillations outlined in the study through the Hoffman lab鈥檚 award-winning submission and , which was recently highlighted in the .
