Workshop on Advanced Statistical Methods and Dynamic Data Banner

June 28th from 9:00 a.m. – 4:40 p.m. EDT

June 30th from 10:00 a.m. – 6:00 p.m. EDT

About This Workshop

The National Institute of Mental Health (NIMH)  hosts a two-day workshop on Advanced Statistical Methods and Dynamic Data Visualizations for Mental Health Studies

NIMH invites basic, translational, and service and intervention researchers from different backgrounds [including but not limited to statisticians, data scientists, computational scientists, quantitative neuroscientists, psychiatrists, psychologists] to register for this timely conference. 

Day one: June 28, 2021, on Advances in Statistical Methods and Applications 

Day two: June 30, 2021, on Dynamic and Interactive Data Visualizations 

The workshop will have two goals:

  1. Address the role of statistical methods in identifying meaningful effects in large neuro-behavioral samples, administrative data, or social media sources. While effects may be statistically significant in large samples, they may only account for a small proportion of the variance, and the opposite is also true.  In addition, estimating causal effects to inform services interventions will still be challenged by confounding and other factors. One objective of this workshop is to identify best practices for evaluating and interpreting meaningful effects in mental health research.
  1. Showcase advanced methods for dynamic and interactive data visualization. One objective of this workshop is to identify potential use cases and gaps for these new dynamic data visualization tools. There will also be opportunities for participants to gain hands-on experience with cutting-edge visualization tools via interactive tutorial sessions.

The workshop is free to attend, but registration is required. Attendees may register for one or both days. Register today!  

For programmatic questions, please contact Michele Ferrante at or Abera Wouhib at

DyNeuSR is a Python visualization library for topological representations of neuroimaging data. DyNeuSR was designed specifically for working with shape graphs produced by the Mapper algorithm from topological data analysis (TDA), as described in this paper “Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis” (Geniesse et al., 2019). Check out this blog post for more about the initial work that inspired the development of DyNeuSR.

yperTools is a Python toolbox designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. The package is built atop many familiar friends, including matplotlib, scikit-learn and seaborn. For a general overview, you may find this talk useful.