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In Journal of neurophysiology ; h5-index 46.0

Modern neurophysiology research requires the interrogation of high-dimensionality datasets. ML/AI workflows have permeated into nearly all aspects of daily life in the developed world, but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community.

Silva Talita de Melo E, Borniger Jeremy, Alves Michele Joana, Alzate Correa Diego, Zhao Jing, Fadda Paolo, Toland Amanda Ewart, Takakura Ana C, Moreira Thiago S, Czeisler Catherine, Otero Jose J


Phox2b, machine learning, random forest, supervised learning, unsupervised learning