USING PRINCIPAL COMPONENT ANALYSIS (PCA) TO INTERPRET ELECTROPHYSIOLOGICAL DATA

Current-clamp recordings capture the membrane potential of excitable cells, such as
neurons, and yield a wealth of information regarding the interplay of diverse ionic
conductances that intricately influence both membrane and action potential.
Extracting parameters from such recordings can unveil the complex interactions
among various ion channels with distinct gating modes and kinetics. The correlation
and co-variation of these parameters are not straightforward to investigate and are
often susceptible to investigator bias. A method capable of objectively unraveling
parameter interactions is Principal Component Analysis (PCA). This technique
detects connections among multiple parameters in an unbiased way. Notably PCA
simplifies an expansive range of variables, rendering intricate multidimensional data
structures more accessible. Although widely adopted in various scientific disciplines,
including biomedical sciences, its application in analyzing electrophysiological data
remains relatively underexplored.In this context, I will illustrate an instance of PCA's
application in analyzing and interpreting electrophysiological data obtained from
auditory brainstem neurons across two developmental phases. Through this
approach, significant features with correlated variations are unveiled, potentially
shedding light on intrinsic developmental programs governing ion channels that
regulate the membrane potential of these neurons.
This work was supported by Fundação para o Amparo a Pesquisa do Estado de São
Paulo (grant# 2019/13458-1).