137 citations · 277 across the 8 of their papers we have counts for
5 papers · 1 filter
A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Data
Sean L. Simpson, Paul J. Laurienti
Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses a…
An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks
Sean L. Simpson, Malaak N. Moussa, Paul J. Laurienti
Group-based brain connectivity networks have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different ment…
Kronecker product linear exponent AR(1) correlation structures and separability tests for multivariate repeated measures
Sean L. Simpson, Lloyd J. Edwards, Martin A. Styner +1
Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifes…
Analysis of 24-Hour Ambulatory Blood Pressure Monitoring Data using Orthonormal Polynomials in the Linear Mixed Model
Lloyd J. Edwards, Sean L. Simpson
The use of 24-hour ambulatory blood pressure monitoring (ABPM) in clinical practice and observational epidemiological studies has grown considerably in the past 25 years. ABPM is a…
Exponential Random Graph Modeling for Complex Brain Networks
Sean L. Simpson, Satoru Hayasaka, Paul J. Laurienti
Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global str…