Citation
Automated Image Analysis Method To Quantify Neuronal Response To Intracortical Microelectrodes

Material Information

Title:
Automated Image Analysis Method To Quantify Neuronal Response To Intracortical Microelectrodes
Series Title:
19th Annual Undergraduate Research Symposium
Creator:
Ward, Ray
Language:
English
Physical Description:
Undetermined

Subjects

Subjects / Keywords:
Center for Undergraduate Research
Center for Undergraduate Research
Genre:
Conference papers and proceedings
Poster

Notes

Abstract:
Intracortical microelectrodes (IMEs) have a wide variety of applications ranging from monitoring single neuron activity to providing a brain-machine interface technology to alleviate the suffering of individuals with devastating neurological disorders. However, the lack of functional reliability has been a major limitation for long-term experiments and clinical implementation. The loss of functionality of IMEs is associated with the formation of glial scar surrounding the implant and a loss of nearby neurons. The quantification of the cell types involved, especially in experiments with large datasets, is a challenging and a time-consuming process. Here we present an optimized, automated method to count cells using FIJI and bin them into desired intervals using Matlab for quantification purposes. Histological sections stained with antibodies to target neuronal nuclei were used to optimize the process. Raw images obtained using confocal microscopy were opened in FIJI and different parameters for image filtering and thresholding were compared to obtain an optimal image for particle analysis. We then compared automatically counted cells with manually counted ones and achieved similar results. Automation reduces variability by processing and analyzing all images with identical settings. This workflow allows several options for customization and provides easily reproducible results while saving time and effort. ( en )
General Note:
Research authors: Ray Ward, Janak Gaire, Kevin J. Otto - University of Florida
General Note:
Faculty Mentor: Intracortical microelectrodes (IMEs) have a wide variety of applications ranging from monitoring single neuron activity to providing a brain-machine interface technology to alleviate the suffering of individuals with devastating neurological disorders. However, the lack of functional reliability has been a major limitation for long-term experiments and clinical implementation. The loss of functionality of IMEs is associated with the formation of glial scar surrounding the implant and a loss of nearby neurons. The quantification of the cell types involved, especially in experiments with large datasets, is a challenging and a time-consuming process. Here we present an optimized, automated method to count cells using FIJI and bin them into desired intervals using Matlab for quantification purposes. Histological sections stained with antibodies to target neuronal nuclei were used to optimize the process. Raw images obtained using confocal microscopy were opened in FIJI and different parameters for image filtering and thresholding were compared to obtain an optimal image for particle analysis. We then compared automatically counted cells with manually counted ones and achieved similar results. Automation reduces variability by processing and analyzing all images with identical settings. This workflow allows several options for customization and provides easily reproducible results while saving time and effort. - Center for Undergraduate Research,

Record Information

Source Institution:
University of Florida
Rights Management:
Copyright Ray Ward. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

UFDC Membership

Aggregations:
University of Florida Institutional Repository

Downloads

This item is only available as the following downloads:


Full Text

PAGE 1

Automated Image Analysis Method to Quantify Neuronal Response to Intracortical Microelectrodes Ray Ward 1 Janak Gaire 2 Kevin Otto 1,2,3,4,5,6 J. Crayton Pruitt Family Department of Biomedical Engineering Correlation Coefficients for Automatic and Manual Counts Image Neuron Count Neuronal Density 106 0.9946 0.9890 107 0.9890 0.9386 112 0.9940 0.8987 113 0.9906 0.9493 117 0.9853 0.8868 120 0.9877 0.9345 Average 0.98932 0.9328 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 106 107 112 113 117 120 Percent Difference Image Average Percent Difference Intro Conclusion Time: Manual count took over 5 hours the automatic process took under 5 minutes. Variance: More variability in manual counts due to fluctuations in user definition of cell, attention, and the effects of adjacent cells on perception. T Test: Difference between mean of manual and automatic counts not statistically significant. Difference: Manual count consistently higher than automatic count. May result from over/under segmentation or unclear potential cells. (See below) Correlation: High correlation indicates clear linear relationship. This method saves time and effort, providing consistent and easily reproducible results for histological quantification Future Work Method could potentially be improved by combining sequences of image filters or using alternative threshold algorithms Apply method to other cell types Examine correlation to fluorescent intensity Use method for histological quantification of IMEs, to better understand the cellular response Intracortical microelectrodes (IMEs) have a wide variety of applications ranging from monitoring neuron activity to treating neurological disorders But the lack of reliable functionality limits their use in long term experiments and clinical implementation Functionality loss is associated with the formation of glial scar around the implant and a loss of nearby neurons 1 Quantification of the cell types involved is challenging and time consuming, particularly in larger datasets Without accurate histological quantification, difficult to accurately describe the relationship between this cellular response and IME functionality Using Fiji and Matlab established cell counting techniques 2 3 can be adapted to automatically quantify the number and density of neurons as a function of distance from the implant References [1] Woolley, AJ, HA Desai, J Gaire AL Ready, and KJ Otto. Intact histological characterization of brain implanted microdevices and surrounding tissue. Journal of Visualized Experiments (72), e50126, doi:10.3791/50126. 2013. [2] I Grishagin Automatic cell counting with ImageJ. Analytical Biochemistry, (473), doi:10.1016/j.ab.2014.12.007. 2015. [3] C Labno Two Ways to Count Cells with ImageJ. University of Chicago Integrated Light Microscopy Core. Retrieved March 20, 2018, from https://www.unige.ch/ medecine / bioimagingfiles /3714/1208/5964/CellCounting.pdf 1 J Crayton Pruitt Family Department of Biomedical Engineering, 2 Department of Neuroscience, 3 Department of Materials Science and Engineering, 4 Department of Neurology, 5 Department of Electrical and Computer Engineering, 6 Nanoscience Institute for Medical and Engineering Technology, University of Florida, Gainesville, FL, USA Methods 6 histological sections of implanted cortical tissue were stained with neuronal nuclei antibodies and imaged using confocal microscopy Image processing was done in Fiji and data analysis was done in Matlab Neuron count and density as a function of distance from implant site was quantified for manually and automatically identified cells using the following workflow : Distance Map Bin Cells by Distance 20m bins Select Site Normalize to Area Neurons / m 2 Particle Analysis Redirect to Distance Map Gaussian Blur 3 pixel radius Auto Threshold Fiji Default Algorithm Fill Holes & Watershed Results Percent Difference Total Counts and Percent Differences Image Automatic Count Manual Count Total Percent Difference 106 722 773 6.60% 107 839 910 7.80% 112 730 765 4.58% 113 565 610 7.38% 117 633 697 9.18% 120 864 965 10.47% Average 725.5 786.67 7.67% Correlation 0.0 0.5 1.0 1.5 2.0 2.5 20 60 100 140 180 220 260 300 340 380 Cell Density (cells/ m 2 ) Distance from Implant Site (m) Auto Count Manual Count Neuron Density vs Distance from Implant Site Image 120 Histological section of implanted tissue showing neuronal nuclei and glial scarring In total, 4720 neurons were identified manually, and 4355 neurons were identified using the automated method The average standard deviation for manual counts ( 7 859 ) was higher than that of automatic counts ( 7 121 ) When Matlab was used to perform two sample t tests for the manual and automatic neuron counts of individual images, the average P value was 0 642 ( = 0 05 ) Examples of (A) under segmentation and (B) over segmentation x10 3