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Research Article
Engineered In Vitro Feed-Forward Networks
Anupama Natarajan1, Thomas B. DeMarse2, Peter Molnar3, and James J. Hickman1*
1The Nanoscience Technology Center, University of Central Florida, Orlando, FL 32826, USA
2Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611 and the NanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA
3The Nanoscience Technology Center, University of Central Florida, Orlando, FL 32826, USA. He is now in the Department of Zoology, University of West Hungary, Szombathely, H-9700, Hungary
Corresponding Author:
James J. Hickman
University of Central Florida
Nanoscience Technology Center 12424 Research Parkway
Suite 400, Orlando, FL 32826 USA Tel: 407-823-1925 Fax: 407-882-2819 E-mail: jhickman@mail.ucf.edu
Received date: October 23, 2012; Accepted date: December 14, 2012; Published date: December 14, 2012
Microelectrode arrays (MEAs) are a promising new method for high throughput neuronal assays. These arrays permit non-invasive, detailed optical and multichannel electrophysiological interrogation of functional neuronal networks for drug development or neurotoxicity assessment. There has also been an effort by a number of groups to develop in vitro analogues of in vivo brain circuitry or physiological systems to serve as well defined models of in vivo tissue. However, a key hurdle in these efforts has been the ability to define and constrain the directionality of pathways within these systems. This issue is particularly relevant during the recreation of in vivo brain architectures that communicate through defined pathways, often with specific directionality. In this paper, we demonstrate a line/ gap topology that promotes the growth of axonal directionally between neurons that have been engineered into a living analogue of a feed-forward neural architecture. The effective connectivity of this architecture was estimated from neural activity measured by a multichannel microelectrode array and quantified using conditional Granger causality analysis. Plasticity was then induced to determine whether 1) LTP/LTD was supported in this novel architecture and 2) whether plasticity differed from random network controls. We show that this method promotes unidirectional feed-forward relative to opposing feedback pathways in spontaneously active networks. This study also represents the first attempt to use the Granger causality metric for the assessment of the activity of a biological neuronal network in which connectivity is highly defined.
