Abdul-Karim et al. IEEE Trans. Image Processing MS Word XP 1 Automatic Selection of Parameters for Vessel/Neurite Segmentation Algorithms

Abstract — An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage, against its conciseness. It enables non-expert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter, and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with 8 parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7 – 21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in descriptio

Optical Monitoring of Neural Network Connectivity Using FM1-43-Evoked Activity from Focal Stimulation of Microelectrode Arrays

This work was supported by the International Collaboration Program NBS-ERC/KOSEF (S.J.K) and by the National Institute of Biomedical Imaging and Bioengineering under Agreement Number R21-EB007782 (M.R.H)

Identification of synaptic activities in microelectrode array-based neural networks

The Microelectrode Arrays (MEAs) have been used for several decades to investigate neuronal networks in vitro. In most of the studies, the neuronal networks have been studied statistically due to complexity of cultured neuronal networks. However, in order to understand the behaviours of neuronal networks dynamically, the identification of synaptic activities of individual neurons is crucial. In this study, we observed individual synaptic activities by utilizing low density neuronal networks arranged orthogonally on MEAs.This work was supported by the International Collaboration Program, NBS-ERC (Nano Bioelectronics and Systems Engineering Research Center)/ KOSEF (Korea Science and Engineering Foundation) and NIH, NS-044287, NSF, ECS-9876771

Modulation of Cultured Neural Networks Using Neurotrophin Release

Polyacrylamide and poly(ethylene glycol) diacrylate hydrogels were synthesized and characterized for use as drug release and substrates for neuron cell culture. Protein release kinetics was determined by incorporating bovine serum albumin (BSA) into hydrogels during polymerization. To determine if hydrogel incorporation and release affect bioactivity, alkaline phosphatase was incorporated into hydrogels and a released enzyme activity determined using the fluorescence-based ELF-97 assay. Hydrogels were then used to deliver a brain-derived neurotrophic factor (BDNF) from hydrogels polymerized over planar microelectrode arrays (MEAs). Primary hippocampal neurons were cultured on both control and neurotrophin-containing hydrogel-coated MEAs. The effect of released BDNF on neurite length and process arborization was investigated using automated image analysis. An increased spontaneous activity as a response to the released BDNF was recorded from the neurons cultured on the top of hydrogel layers. These results demonstrate that proteins of biological interest can be incorporated into hydrogels to modulate development and function of cultured neural networks. These results also set the stage for development of hydrogel-coated neural prosthetic devices for local delivery of various biologically active molecules.This work was supported by the International Collaboration Program, Nano Bioelectronics and Systems Engineering Research Center/Korea Science and Engineering Foundation (R11-2000-075-00002-0), by the Nanobiotechnology Center (NBTC), an STC Program of the National Science Foundation under agreement no. ECS-9876771, the National Institutes of Health under agreement no. R01-NS044287 (WS) and by the National Institute of Biomedical Imaging and Bioengineering under agreement no. R21EB007782 (MRH). The computational image analysiswas supported by the Center for Subsurface Sensing and Imaging Systems (NSF EEC- 9986821). The authors acknowledge use of the Wadsworth Center Advanced Light Microscopy & Image Analysis Core Facility. They would also like to thank Shirley Madewell and Adriana Verschoor for critical review of the manuscript