Identification and assessment of systematic measurement error on electrophysiological recordings of neural cell cultures

Abstract

Microelectrode arrays (MEA) hold great promise for a broad range of applications that require reliable characterization of the growth and function of neurons in culture. Widespread adoption of this platform depends on analytical methods to extract meaning from highly variable and noisy observations. In analyzing a comprehensive database of MEA recordings, we discovered that 22% of the electrodes presented systematic patterns of under- or non-detection of spike activity. Going undetected, principal components analysis (PCA) of these data reveal trends that would have lead to incorrect biological interpretations. We fully document these defective or biased electrodes, and distinguish two forms of defectiveness, via representations that aid in detecting them. We also showcase our approach for analyzing these data that permit for post-analytic review and correction. Repeating our PCA on cleaned data, we discover a more complex interplay of biological variability. Finally, we make a case for transparency in data reporting and propose best practices for experimental and analysis phases.