From 669bd3723aa6f57ce1b1e891defaffe80beea5f8 Mon Sep 17 00:00:00 2001 From: Caleb Geniesse Date: Wed, 12 Jan 2022 17:07:39 -0800 Subject: [PATCH] Update index.md --- docs/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/index.md b/docs/index.md index cb0960d..4077cfc 100644 --- a/docs/index.md +++ b/docs/index.md @@ -15,7 +15,7 @@ NeuMapper was designed specifically for working with complex, high-dimensional n -For more details about NeuMapper see "[NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain's Dynamical Organization](https://doi.org/10.1162/netn_a_00229)" (Geniesse et al., 2021). 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. +For more details about NeuMapper see "[NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain's Dynamical Organization](https://doi.org/10.1162/netn_a_00229)" (Geniesse et al., 2022). 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. @@ -241,7 +241,7 @@ 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://doi.org/10.1162/netn_a_00229). *Network Neuroscience*, Advance publication. doi:10.1162/netn_a_00229 +> Geniesse, C., Chowdhury, S., & Saggar, M. (2022). [NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain's Dynamical Organization](https://doi.org/10.1162/netn_a_00229). *Network Neuroscience*, Advance publication. doi:10.1162/netn_a_00229 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*, *3*(3). doi:10.1162/netn_a_00093