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

Silva, Talita M, Borniger, Jeremy C, Alves, Michele Joana, Alzate Correa, Diego, Zhao, Jing, Fadda, Paolo, Toland, Amanda Ewart, Takakura, Ana C, Moreira, Thiago S, Czeisler, Catherine M, Otero, José Javier (April 2021) Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice. Journal of Neurophysiology, 125 (4). pp. 1164-1179. ISSN 0022-3077

URL: https://www.ncbi.nlm.nih.gov/pubmed/33502943
DOI: 10.1152/jn.00155.2020

Abstract

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.

Item Type: Paper
Subjects: organism description > animal behavior > REM sleep
bioinformatics > computational biology > algorithms > machine learning
neurobiology > neuroscience
CSHL Authors:
Communities: CSHL labs > Borniger lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 1 April 2021
Date Deposited: 30 Apr 2021 14:19
Last Modified: 02 Aug 2021 18:48
PMCID: PMC8282220
Related URLs:
URI: https://repository.cshl.edu/id/eprint/39961

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