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Neuroscience PhD at Wake Forest University at Wake Forest University

Wake Forest University Graduate School » Neuroscience PhD at Wake Forest University


McCrory MC;Gower EW;Simpson SL;Nakagawa TA;Mou SS;Morris PE;  Off-hours admission to pediatric intensive care and mortality; Pediatrics; 2014 Nov; 134:e1345-e1353
Edwards LJ;Simpson SL;  An analysis of 24-h ambulatory blood pressure monitoring data using orthonormal polynomials in the linear mixed model; Blood Press Monit ; 2014 Jun; 19:153-163
Simpson SL;Edwards LJ;Styner MA;Muller KE;  Kronecker product linear exponent AR(1) correlation structures for multivariate repeated measures; PLoS ONE; 2014; 9:e88864-
Simpson SL;Edwards LJ;Styner MA;Muller KE;  Separability tests for high-dimensional, low sample size multivariate repeated measures data; J Appl Stat; 2014; 41:2450-2461
Simpson SL;Edwards LJ;  A circular LEAR correlation structure for cyclical longitudinal data; Stat Methods Med Res; 2013 Jun; 22:296-306
Bruce MA;Beech BM;Crook ED;Sims M;Griffith DM;Simpson SL;Ard J;Norris KC;  Sex, weight status, and chronic kidney disease among African Americans: the Jackson Heart Study; J Investig Med; 2013 Apr; 61:701-707
Simpson SL;bowman fd;Laurienti PJ;  Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain; Statistics Survey; 2013; 7:1-36
Simpson SL;Lyday RG;Hayasaka S;Marsh AP;Laurienti PJ;  A permutation testing framework to compare groups of brain networks; Front Comput Neurosci ; 2013; 7:171-
Vaughan L;Leng X;Dagenbach D;Resnick S;Rapp SR;Jennings JM;Brunner RL;Simpson SL;Beavers DP;Coker LH;Gaussoin SA;Sink KM;Espeland MA;  Intraindividual Variability in Domain-Specific Cognition and Risk of Mild Cognitive Impairment and Dementia; Current Gerontology and Geriatrics Research; 2013; 2013:
Simpson SL;Moussa MN;Laurienti PJ;  An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks; Neuroimage ; 2012 Apr 2; 60:1117-1126


My main research foci are in network-based neuroimaging and health disparities. Network analyses aim to characterize the systemic structure of the brain and how this structure relates to various conditions (e.g., disease) and behaviors. Understanding this relationship between brain structure, composed of the interactions between brain regions, and brain function necessitates statistical methodologies that account for data complexity. That is, the statistical evaluation of systemic properties of brain networks requires tools that can capture the complex interactions present in networks. Despite this, most current brain network studies employ rudimentary statistical approaches. There is a pressing need for further statistical development in order to engender powerful analytical tools that will leverage the wealth of data present in brain networks and aid in our understanding of normal and abnormal brain function. My focus on fusing novel statistical methods with network-based neuroimage analysis aims to help address this need.