Very happy that I have been awarded a €250k Veni grant for developing a multi-timescale network modelling framework!
To reduce the burden of depression, symptoms should be studied at the appropriate timescales. Current methods cannot handle variables with different measurement frequencies. This project aspires to develop a multi-timescale network modeling framework to cross current methodological borders to simultaneously investigate symptoms at the time scales at which they evolve.
Therefore, these three objectives are crucially important:
Develop a formal theoretical model for depression, which is essential to determine what data is needed to study depression
Develop a network estimation method that crosses current methodological borders to allow the estimation of network structure among fast and slow-changing variables in one network
Empirically test the formal theoretical model with the multi-timescale network estimation method by collecting and analyzing unique data with longitudinal measurements on different timescales and existing datasets
I would like to thank all colleagues, reviewers, and committee members who took the time to provide their valuable feedback and insights. Looking forward to work on this project in the years to come!
Since it is possible again to run NetLogo models in your browser, I would like to publish one of my good old NetLogo models: Vulnerability to Depression. The nice thing about it is that you can play around and let the model run while adjusting several things. For example, you can adjust the connection strength, you can exert stress on the network, and you can apply a ‘shock’ to the system as if you are administering electroconvulsive therapy. On the Model Info page (see the tab at the bottom), you can find a lot of info and suggestions to explore this model. Especially the hysteresis effect is worth exploring. You can find a link to the model here. Have fun!
On December 18, the Van Swinderen Prize was awarded during the 1821st meeting of the Royal Physics Society Groningen for best thesis and being able to reach out to a general (academic) public. Based on presentations of the four nominees on their research, I won the second prize and received € 1000!
The NetworkComparisonTest package has now been updated on GitHub (link here) to version 2.1.1. In this new version, I added an argument “p.adjust.methods”. With this argument you can specify how to control for multiple edge testing to control type I error. You can choose one of “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, or “none”. If you do not specify this argument, it uses the default “none”.
In the older version, a Holm-Bonferroni correction was applied. However, when doing exploratory analyses, one might be interested in the uncorrected p-values. If you are doing hypothesis testing, you can now choose your own method of correction. See the help page of the p.adjust() function in R for more information about the different methods.
If you encounter problems or bugs, please let me know: email@example.com. I will upload the new version to CRAN soon.
Check out this interesting blog of our Psychosystems Group regarding a recently accepted paper that purports to show that network structures do not replicate across datasets. To quote a statement of the blog in response to the paper:
“…state-of-the-art networks don’t just replicate – they replicate with stunning precision.”