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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice

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Silva, Talita M. [1, 2] ; Borniger, Jeremy C. [3] ; Alves, Michele Joana [2] ; Correa, Diego Alzate [2] ; Zhao, Jing [4] ; Fadda, Paolo [5] ; Toland, Amanda Ewart [5, 6] ; Takakura, Ana C. [7] ; Moreira, Thiago S. [1] ; Czeisler, Catherine M. [2] ; Otero, Jose Javier [2]
Total Authors: 11
[1] Univ Sao Paulo, Inst Biomed Sci, Dept Physiol & Biophys, Sao Paulo - Brazil
[2] Ohio State Univ, Coll Med, Dept Pathol, Div Neuropathol, Columbus, OH 43210 - USA
[3] Cold Spring Harbor Lab, POB 100, Cold Spring Harbor, NY 11724 - USA
[4] Ohio State Univ, Coll Dent, Dept Biomed Informat, Columbus, OH 43210 - USA
[5] Ohio State Univ, Genom Shared Resource Comprehens Canc Ctr, Columbus, OH 43210 - USA
[6] Ohio State Univ, Coll Med, Dept Canc Biol & Genet, Columbus, OH 43210 - USA
[7] Univ Sao Paulo, Inst Biomed Sci, Dept Pharmacol, Sao Paulo - Brazil
Total Affiliations: 7
Document type: Journal article
Source: Journal of Neurophysiology; v. 125, n. 4, p. 1164-1179, APR 2021.
Web of Science Citations: 0

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (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. NEW \& NOTEWORTHY ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards. (AU)

FAPESP's process: 15/23376-1 - Retrotrapezoid nucleus, respiratory chemosensitivity and breathing automaticity
Grantee:Thiago dos Santos Moreira
Support type: Research Projects - Thematic Grants
FAPESP's process: 18/03994-0 - Ultrastructural analyses of axon pathology in PHOX2b-astrocyte ablated mice
Grantee:Talita de Melo e Silva
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 19/01236-4 - Effects of pharmacological and non-pharmacological treatments on respiratory changes observed in a murine model of Parkinson's disease
Grantee:Ana Carolina Thomaz Takakura
Support type: Regular Research Grants