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Exploring sources of variability in electrophysiology data of mammalian neurons

University of British Columbia

Recently, there has been a major effort by neuroscientists to systematically organize and integrate vast quantities of brain data. However, electrophysiological properties have been shown to be sensitive to experimental conditions, thus directly comparing them between experiments could lead to inconsistent results. Here, I characterize the general effects of experimental solution composition differences on the reported ephys measurements. For that purpose, I employ text-mining, supplemented with manual curation to gather experimental solution information from published neurophysiological articles. I integrate the extracted information into the existing NeuroElectro database, which contains the electrophysiology, neuron type and experimental conditions information (temperature, electrode type, animal age, etc.) from the above neuroscientific literature. Exploring commonly used experimental solution recipes, I found the effect of solution compositions of explaining variance in electrophysiological properties to be small, relative to the amount of the existing ephys variability. Then, I created models for predicting the variability of ephys properties commonly reported by neurophysiologists, using the available experimental conditions information. These models can be used to remove a portion of the ephys variance when comparing results from different experiments, generally making such comparisons more reliable. To validate their performance, I adjusted a portion of NeuroElectro data to experimental conditions used by Allen Institute for Brain Science and compared the respective ephys properties before and after the adjustment.

Text

2017-07-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60253

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/