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Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates.

Ellese Cotterill¹, Diana Hall², Kathleen Wallace²

¹Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Journal of Biomolecular Screening

|March 31, 2016

Summary

Neural network development in vitro was tracked using multiwell microelectrode arrays (mwMEAs). Key activity features accurately predict network age, enabling efficient neurodevelopment studies.

Keywords:

cell-based assays membrane potential neurological diseases toxicology

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Area of Science:

Neuroscience
Developmental Biology
Bioengineering

Background:

Neural network development in vitro is crucial for understanding neurodevelopmental processes.
Microelectrode array (MEA) technology allows for the recording of neural activity.
Multiwell MEA (mwMEA) systems offer higher throughput for such recordings.

Purpose of the Study:

To characterize neural network ontogeny in primary cortical cultures using mwMEA recordings.
To identify key features of neural activity that indicate developmental stage.
To validate the use of mwMEAs for studying neurodevelopment and screening potential treatments.

Main Methods:

Utilized multiwell microelectrode array (mwMEA) recordings from primary cortical cultures over 12 days in vitro (DIV).
Analyzed action potential spiking activity, bursting parameters, synchrony, and network bursting.
Employed principal components analysis (PCA) and machine learning classifiers (random forest, support vector machines) to analyze network activity features.
Developed a novel resampling approach for determining sample size in treatment comparisons.

Main Results:

Neural network activity showed rapid development and increased organization between DIV 5 and 12.
PCA segregated data by age, indicating distinct developmental stages.
Four specific features (CV of within-burst ISI, CV of interburst interval, network spike rate, burst rate) predicted recording age with >65% accuracy.
Binary age classification achieved up to 95% accuracy.
mwMEA network development mirrored single-well MEA development.

Conclusions:

Neural network development on mwMEA plates is comparable to single-well MEAs.
Specific quantifiable features reliably indicate the developmental age of neural networks.
The high throughput of mwMEAs is suitable for screening drugs and chemicals affecting neurodevelopment.
This platform can advance research into neurodevelopmental disorders and therapeutic interventions.