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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# Network Analysis: A comparative Analysis of Centrality Measures for the Brain Subregions of two Populations of Prairie Voles
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## Background
Translational research involving animals offers a great opportunity for understanding human behaviors that would otherwise be impossible through a direct study conducted on humans. However, the success of translational research depends largely on the use of appropriate animal models since typical laboratory animals do not exhibit much of the complex social behaviors observed in humans. This is where the prairie vole (Microtus ochrogaster) comes in handy as it has been found to provide an extraordinary animal model that can be used to study several aspects of social cognition, because they establish long-term socially monogamous relationships with mates, provide biparental care and in natural habitats, alloparental behavior is commonly observed. The prairie vole model has already contributed to the better understanding of the neurobiology and genetics of social bonding, parental care, social buffering, the effects of early life experience in later social behavior and social related depression (Ortiz et al., 2018).
## Objective
Utilizing functional magnetic resonance imaging (fMRI) data from culturally and behaviorally distinct populations of prairie voles generated by Ortiz et al. (2021), this report conducts a statistical comparison of each of the centrality measures (degree, closeness, betweenness, eigenvector centrality), comparing the two populations of voles (IL and KI). IL and KI denote the prairie male voles from Illinois and male cross-breed off-springs of Kansas dam and Illinois sires, respectively. The goal is to determine if the two populations of prairie voles exhibit any significant differences in prosocial behavior evident from differences in the centrality measures for particular subregions of the brain networks.
<!-- Ortiz et. al (2021) found Illinois prairie males to be more sociable than KI males and differential connectivity accounts for differential expression of prosociality and aggression, I hypothesized that differences in some of the centrality measures for the midbrain subregion will definitely be observed. -->
<!-- ## Task: -->
<!-- conduct a statistical comparison of each of the centrality measures (degree, closeness, betweenness, eigenvector centrality), comparing the two populations of voles (IL and KI). Use proper statistical methodology and terminology. Justify your statistical approach by citing relevant assumptions, parametric assumptions, or violations. Visualize the graph effectively to make the information as easy to read as possible. -->
## Source of data and data description
The data were provided by Richard J. Ortiz, a Research Technician at the Department of Biological Sciences at UTEP, as an excel workbook consisting of three main sheets relevant to our analysis. First sheet contains IL voles brain network data in the form of an adjacency matrix with 111 nodes produced using a vole-specific atlas. The second sheet contains similar data for the KI voles whiles the third provides a list of all nodes which are the region of interest (ROIs) and their corresponding subregions. Edges in the network data are weighted by the absolute Pearson's correlation coefficients across all node pairs where a **2.3** threshold was used to avoid weak node connections and zero means no connection between any two pairs of nodes.
The various results obtained in this report are highly reproducible not only for the `midbrain` subregion but for all the other subregions as well; first because all the R codes generating the results are placed side-by-side with the report documentation in an accompanying RMarkdown file, and second via an online web application where the user can interact with to see real time results. To replicate the results for any other subregion, simply provide the name of the particular subregion under the `Global settings` at the beginning of the RMarkdown file. Alternatively, just select any subregion on the online web application. Below is a link to the online web application:
> [https://william-agyapong.shinyapps.io/vbna/](https://william-agyapong.shinyapps.io/vbna/)
All statistical analyses and visualizations were done in the R statistical software.
# Refernces
- Ashtiani, M., Salehzadeh-Yazdi, A., Razaghi-Moghadam, Z., Hennig, H., Wolkenhauer, O., Mirzaie, M., & Jafari, M. (2017). A systematic survey of centrality measures for protein-protein interaction networks. bioRxiv, 149492.
- Richard J. Ortiz, Amy E. Wagler, Jason R. Yee, Praveen P. Kulkarni, Xuezhu Cai, Craig F. Ferris, Bruce S. Cushing, Functional connectivity differences between two culturally distinct prairie vole populations: insights into the prosocial network, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2021, ISSN 2451-9022, https://doi.org/10.1016/j.bpsc.2021.11.007. (https://www.sciencedirect.com/science/article/pii/S2451902221003207)
- Ortiz, J.J., Portillo, W., Paredes, R.G. et al. Resting state brain networks in the prairie vole. Sci Rep 8, 1231 (2018). https://doi.org/10.1038/s41598-017-17610-9
- Network Analysis in R by Dai Shizuka: https://dshizuka.github.io/networkanalysis/tutorials.html
- Centrality measures: https://cambridge-intelligence.com/keylines-faqs-social-network-analysis/
- https://bookdown.org/markhoff/social_network_analysis/your-first-network.html
- https://kateto.net/netscix2016.html