Update README.md

README.md

@@ -10,6 +10,8 @@ Painlessly plot networks on a brain in python. The package is still in early dev
 
 [See homepage for more details, examples, documentation](https://wiheto.github.io/netplotbrain/)
 
+[See Network Neuroscience paper for a general turorial](https://doi.org/10.1162/netn_a_00313)
+
 ![](https://raw.githubusercontent.com/wiheto/netplotbrain/main/examples/figures/showcase.png)
 
 ## Note
@@ -44,22 +46,22 @@ Suggestion of text in methods section:
 
 >### Methods
 >
->Network plots were generated using netplotbrain (Thompson & Fanton 2021), which utilizes templateflow (Ciric et al 2021), matplotlib (Hunter 2007), pandas (McKinney 2011), numpy (Harris et al 2020), scipy (Virtanen et al 2020), nibabel (Brett et al 2020), and scikit-image (van der Walt et al 2014).
+>Network plots were generated using netplotbrain (Fanton & Thompson 2023), which utilizes templateflow (Ciric et al 2021), matplotlib (Hunter 2007), pandas (McKinney 2011), numpy (Harris et al 2020), scipy (Virtanen et al 2020), nibabel (Brett et al 2020), and scikit-image (van der Walt et al 2014).
 >
 >### References
 >
 >Brett, Matthew, Markiewicz, Christopher J., Hanke, Michael, Côté, Marc-Alexandre, Cipollini, Ben, McCarthy, Paul, et al (2020, November 28). nipy/nibabel: 3.2.1 (Version 3.2.1). Zenodo. http://doi.org/10.5281/zenodo.4295521
 >
 >Ciric, R., Lorenz, R., Thompson, W. H., Goncalves, M., MacNicol, E., Markiewicz, C., ... & Esteban, O. (2021). TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging. bioRxiv.
 >
+>Fanton, S., & Thompson, W. H. NetPlotBrain: A Python package for visualising networks and brains. Network Neuroscience, 1-21.
+>
 >Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362.
 >
 >Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. IEEE Annals of the History of Computing, 9(03), 90-95.
 >
 >McKinney, W. (2011). pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14(9), 1-9.
 >
->Thompson WH, & Fanton S. (2021, 10 March). wiheto/netplotbrain:. Zenodo. doi :10.5281/zenodo.4593837
->
 >Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., ... & Yu, T. (2014). scikit-image: image processing in Python. PeerJ, 2, e453.
 >
 >Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... & van Mulbregt, P. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods, 17(3), 261-272.