Register now for one or both of the following two upcoming Webinars in April sponsored (or co-sponsored) by the Mental Health Statistics Section
Title: Introduction to Functional Neuroimaging
Presenter: Martin Lindquist
Date and Time: Thursday, April 13, 2017, 11:00 a.m. – 1:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section
Registration Deadline: Tuesday, April 11, at 12:00 p.m. Eastern time
Description:
Understanding the brain is arguably among the most complex, important and challenging issues in science today. Neuroimaging is an umbrella term for an ever-increasing number of minimally invasive techniques designed to study the brain. These include a variety of rapidly evolving technologies for measuring brain properties, such as structure, function and disease pathophysiology. The analysis of neuroimaging data is an example of a modern ‘big data’ problem, and the data is not only large but also has a complex correlation structure in both space and time. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists. In this talk we will focus on methods for performing functional neuroimaging (e.g., functional MRI) and discuss how these techniques can be used to detect areas of the brain activated by a task, determine how different brain regions are connected and communicate with one another, and how brain measurements can be used for prediction and classification purposes.
Title: Intensive Longitudinal Data Analysis Using Mplus
Presenters: Bengt Muthen, Tihomir Asparouhov, and Ellen Hamaker, University of California, Los Angeles
Date and Time: Thursday, April 20, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section
Registration Deadline: Tuesday, April 18, at 12:00 p.m. Eastern time
Description:
This talk discusses new methods for analyzing intensive longitudinal data, such as obtained with ecological momentary assessments, experience method sampling, ambulatory assessments, and daily diaries. Typically, such data have a large number of time points, T = 20-150. Single-level (N=1) as well as multilevel (N > 1) time series models with random effects varying across subjects are handled using a dynamic structural equation model (DSEM) and Bayesian estimation implemented in the Mplus Version 8 software. DSEM for N=1 time series analysis can be used to model the dynamics within a particular individual over time. Additionally, N > 1 multilevel DSEM includes extensions of time series models, such that at level 1 a time series model is used to model the within-person dynamics of a process over time, while at level 2 individual differences in the parameters that capture these dynamics are modeled. DSEM can handle multivariate outcomes as well as latent variables, and random effects can be both predicted from but also predictors of level 2 variables. DSEM is available with auto-regressive and moving-average components both for observed-variable models such as regression and cross-lagged analysis and for latent variable models such as factor analysis, IRT, structural equation modeling, and mixture modeling. DSEM also handles time-varying effect modeling (TVEM) where parameters change not only across individuals but also across time, making it suitable for assessing intervention effects. Several examples are discussed from application areas such as:
- multilevel AR(1) model with random mean, random AR, and random variance
- multilevel AR(1) model with measurement error
- multilevel ARMA(1,1) model
- multilevel cross-lagged modeling
- multilevel AR modeling with a trend
- latent multilevel AR(1) model with multiple indicators
- latent multilevel VAR(1) model and dynamical networks
- dynamic SEM
- dynamic latent class analysis using hidden Markov and Markov-switching AR models
Each registration is allowed one web connection and one audio connection. For a single registration fee, multiple persons can view each connection, for example, by projecting the webinar in a conference room.
For additional details and to register, go to http://www.amstat.org/education/weblectures/index.cfm.
------------------------------
Douglas Gunzler, PhD
Assistant Professor of Medicine
Case Western Reserve University
Cleveland, Ohio
------------------------------