Lecture: Modelling and inference methods for brain imaging research
Schedule
11:30-12:00: Registration & Coffee
12:00-1:00 pm: Lecture, Population Neuroimaging, Thomas Nichols
1:00-1:30 pm: Lunch (free)
1:30-2:30 pm: Tutorial on neuroimaging meta-analysis, Thomas Nichols
2:30-3:00 pm: Reception & coffee
Abstract
Brain imaging studies have traditionally struggled to break into 3-digit sample sizes: e.g., a recent Functional Magnetic Resonance Imaging (fMRI) meta-analysis of emotion found a median sample size of n=13. However, we now have a growing collection studies with sample sizes with 4-, 5- and even 6-digits. Many of these "population neuroimaging" studies are epidemiological in nature, trying to characterise typical variation in the population to help predict health outcomes across the life span. Dr Nichols will discuss some of the challenges these studies present, in terms of massive computational burden but also in ways that they expose shortcomings of existing mass univariate techniques. Dr Nichols will also discuss how these datasets present intriguing methodological problems heretofore absent from neuroimaging statistics. For example, the "null hypothesis fallacy" is how H0 is never strictly true, and yet with 100,000 subjects you'll eventually find some effect even if it is meaningless. This motivates work spatial confidence sets on meaningful effect sizes (instead of thresholding test statistic images), providing intuitive measures of spatial uncertainty.
Bio
is the Professor of Neuroimaging Statistics and a Wellcome Trust Senior Research Fellow in Basic Biomedical Science at the Oxford University, Big Data Institute.
Dr Nichols is a statistician with a solitary focus on modelling and inference methods for brain imaging research. He has a unique background, with both industrial (Director, Modelling and Genetics, GlaxoSmithKline) and academic experience, and diverse training including computer science, cognitive neuroscience and statistics.
The focus of Dr. Nichols work is developing modelling and inference methods for brain image data. He has worked with a variety of types of data, including Positron Emission Tomography and Magneto- and Electroencephalography, though most of his methods are motivated by Magnetic Resonance Imaging (MRI) and functional MRI (fMRI) in particular. He has extensive experience in modelling large, complex data, particularly known for his contributions to multiple testing inference for brain imaging. He has developed methods for clinical trials with imaging, as well as methods for integrating genetic and imaging data. His current research involves meta-analysis of neuroimaging studies and informatics tools to make data-sharing easy and pervasive.
聽-聽the聽event is聽free聽but registration is required.
