I just returned from a fun event-filled couple days at Wright State Univeristy in Dayton, Ohio, visiting statistician Harry Khamis. Harry invited me to give two talks on Friday, November 2nd, 2012: one in Statistics and a second in Neuroscience, arranged by Thomas N. Hangartner. Harry was the model host; I always felt taken care of, my needs met.
I was excited to meet two people from my talks who could use the methods I presented. Prof Nasser H Kashou develops models for HRF functions, which the SimTB might be helpful for. Prof Yvonne Vadeboncoeur uses stable isotopes to study freshwater ecosystems, and we had some exciting discussion about collaborative opportunities.
The links to the papers the talks draw on are at the bottom.
My morning neuroimaging talk (10:15) in the Department of Biomedical, Industrial & Human Factors Engineering (BIE) included two-and-one-half topics: SimTB, subject variability with GICA, and a little data visualization.
Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study
A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to intersubject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data. mialab.mrn.org/software/simtb
My afternoon statistics talk (3:00) in the Department of Mathematics and Statistics to a packed room (they had to bring in additional chairs!) included work that extends my published stable isotope sourcing work.
An extended Bayesian stable isotope mixing model for trophic level inference
You are what and where you eat on the food web. We developed an extended Bayesian mixing model to jointly infer organic matter utilization and isotopic enrichment of organic matter sources in order to infer the trophic levels of several numerically abundant fish species (consumers) present in Apalachicola Bay, FL, USA. Bayesian methods apply for arbitrary numbers of isotopes and diet sources but existing models are somewhat limited as they assume that trophic fractionation is estimated without error or that isotope ratios are uncorrelated. The model uses stable isotope ratios of carbon, nitrogen, and sulfur, isotopic fractionations, elemental concentrations, elemental assimilation efficiencies, as well as prior information (expert opinion) to inform the diet and trophic level parameters. The model appropriately accounts for uncertainly and prior information at all levels of the analysis.
Summary of both SimTB papers.
SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability
Erik B. Erhardt, Elena A. Allen, Yonghua Wei, Tom Eichele, Vince D. Calhoun
NeuroImage 59 (2012), pp. 4160-4167
Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study
Elena A. Allen, Erik B. Erhardt, Yonghua Wei, Tom Eichele, Vince D. Calhoun
Data visualization in the neurosciences: overcoming the curse of dimensionality
Elena A. Allen, Erik B. Erhardt, Vince D. Calhoun
A Bayesian framework for stable isotope mixing models
Erik B. Erhardt and Edward J. Bedrick
Environmental and Ecological Statistics
Erik Barry Erhardt, PhD, is an Assistant Professor of Statistics at the University of New Mexico Department of Mathematics and Statistics, where he serves as Director of the statistics consulting clinic. His research interests include Bayesian and frequentist statistical methods for stable isotope sourcing and brain imaging. Erik is a Howard Hughes Medical Institute Interfaces Scholar collaborating in interdisciplinary research and consulting. StatAcumen.com
MIND, Research, stable isotopes, Statistics