TOPIC Spatial Bayesian Point Process Modelling for Neuroimaging Data 
AREA Signal Processing & New Media  
SPEAKER Thomas Nichols PhD, Department of Statistics & Warwick Manufacturing Group, University of Warwick 
DATE 18 March 2015, Wednesday 
TIME 2.30 pm to 3.30 pm 
VENUE CeLS Seminar Room 2 
FEES No Charge 

The standard approach to brain imaging data analysis is a mass-univariate one: At each voxel a linear model is fit, ignoring all other voxels. While this has obvious computational advantages, it cannot directly capture spatial nature of brain activations that are of interest. I this talk I will review several lines of research that explicitly model the spatial variation in brain image data using point processes. For Coordinate-Based Meta-Analysis (CBMA) data, we use a hierarchical point process approach, modelling brain activation as a mixture of latent activation centers, and these activation centers are in turn modelled as off-spring from latent population centers. This approach allows inference on the location of population centers, separately estimating the uncertainty of the population location and the uncertainty of individual activation center''s location about that population center (akin to the distinction between standard error and standard deviation). We also consider non-hierarchical models for CBMA, that allow "meta regression", accounting for study-to-study differences with covariates like year. Finally, we show how these approaches naturally encompasses other types of coordinate-valued data, such as Multiple Sclerosis white matter lesions. Joint with Tim Johnson, University of Michigan Biostatistics. 

Dr. Nichols is a Professor, Wellcome Trust Senior Research Fellow, and the Head of Neuroimaging Statistics at the University of Warwick, holding a joint position between Warwick Manufacturing Group & the Department of Statistics. He is a statistician with a solitary, 20-year focus on modelling and inference methods for brain imaging research. Before graduate studies, he worked as a programmer and statistician at the University of Pittsburgh''s Positron Emission Tomograpy Facility. He earned his PhD in Statistics at Carnegie Mellon University with cross-training in Cognitive Neuroscience, and in 2000 joined the faculty at the Department Biostatistics at the University of Michigan. He had a 3 year sojurn in industry, working at GlaxoSmithKline''s Clinical Imaging Centre, London, where he developed methods for fMRI clinical trials and imaging genetics studies. He is a developer of both the Statistical Parametric Mapping (SPM) and FMRIB Software Library (FSL) tools, and is well known for bringing advanced statistical methodology to brain imaging and making it accessible to non-statisticians. In 2009 he received the Wiley Young Investigator Award by the Organization for Human Brain Mapping in recognition for his contributions to statistical modeling & inference of neuroimaging data. 


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