A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings

TL;DR

This paper introduces a deep learning framework for classifying in vitro neuronal recordings from MEAs, achieving high accuracy and outperforming traditional feature-based methods, with potential applications in genetic and drug response analysis.

Contribution

The paper presents a novel deep learning approach that effectively classifies neuronal MEA recordings from different genotypes without relying on handcrafted features.

Findings

16.69% accuracy improvement over Logistic Regression

95.91% classification accuracy on mouse MEA data

Effective generalization for different neuronal mutations

Abstract

Multi-Electrode Arrays (MEAs) have been widely used to record neuronal activities, which could be used in the diagnosis of gene defects and drug effects. In this paper, we address the problem of classifying in vitro MEA recordings of mouse and human neuronal cultures from different genotypes, where there is no easy way to directly utilize raw sequences as inputs to train an end-to-end classification model. While carefully extracting some features by hand could partially solve the problem, this approach suffers from obvious drawbacks such as difficulty of generalizing. We propose a deep learning framework to address this challenge. Our approach correctly classifies neuronal culture data prepared from two different genotypes -- a mouse Knockout of the delta-catenin gene and human induced Pluripotent Stem Cell-derived neurons from Williams syndrome. By splitting the long recordings into short slices for training, and applying Consensus Prediction during testing, our deep learning approach improves the prediction accuracy by 16.69% compared with feature based Logistic Regression for mouse MEA recordings. We further achieve an accuracy of 95.91% using Consensus Prediction in one subset of mouse MEA recording data, which were all recorded at six days in vitro. As high-density MEA recordings become more widely available, this approach could be generalized for classification of neurons carrying different mutations and classification of drug responses.