Update README.md

added net neu paper link

README.md

@@ -9,7 +9,7 @@ Developed with neuroimaging data analysis in mind, NeuMapper implements a novel
 
 NeuMapper was designed specifically for working with complex, high-dimensional neuroimaging data and produces a shape graph representation that can be annotated with meta-information and further examined using network science tools. These shape graphs can be visualized using [DyNeuSR](https://braindynamicslab.github.io/dyneusr/), a Python visualization library that provides a custom web interface for exploring and interacting with shape graphs, and several other tools for anchoring these representations back to neurophysiology and behavior. To see how NeuMapper and DyNeuSR can be used together to create beautiful visualizations of high-dimensional data, check out the [examples](https://github.com/braindynamicslab/neumapper/tree/master/examples/) folder.
 
-For more details about the original Mapper algorithm and related applications to neuroimaging data, see "[Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis](https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093)" (Geniesse et al., 2019) and "[Towards a new approach to reveal dynamical organization of the brain using topological data analysis](https://www.nature.com/articles/s41467-018-03664-4)" (Saggar et al., 2018). Check out this [blog post](https://braindynamicslab.github.io/blog/tda-cme-paper/) for more about the initial work that inspired the development of NeuMapper. 
+For more details about NeuMapper see "[NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain's Dynamical Organization (in-press) Network Neuroscience](https://direct.mit.edu/netn)" . For the original Mapper algorithm and related applications to neuroimaging data, see "[Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis](https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093)" (Geniesse et al., 2019) and "[Towards a new approach to reveal dynamical organization of the brain using topological data analysis](https://www.nature.com/articles/s41467-018-03664-4)" (Saggar et al., 2018). Check out this [blog post](https://braindynamicslab.github.io/blog/tda-cme-paper/) for more about the initial work that inspired the development of NeuMapper. 
 
 
 
@@ -229,7 +229,7 @@ dG.visualize('haxby_decoding_neumapper_dyneusr.html')
 ## **References**
 
 If you find NeuMapper useful, please consider citing:
-> Geniesse, C., Chowdhury, S., Saggar, M. (2021). [NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain's Dynamical Organization](https://direct.mit.edu/netn). *Network Neuroscience*. Under revision. 
+> Geniesse, C., Chowdhury, S., Saggar, M. (2021). [NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain's Dynamical Organization](https://direct.mit.edu/netn). *Network Neuroscience*. In press. 
 
 For more information about DyNeuSR, please see:
 > Geniesse, C., Sporns, O., Petri, G., & Saggar, M. (2019). [Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis](https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093). *Network Neuroscience*. Advance publication. doi:10.1162/netn_a_00093