Tuesday, Jan.21, 2020
- 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
Register via Eventbrite – the event is free but registration is required.
Open to faculty and students from McGill University, Concordia University, University of Montreal and UQAM, and staff from local businesses interested in brain imaging.
Jeanne Timmins Amphitheatre @ The Neuro, 3801 University St, Montréal, QC, H3A 2B4
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.
Thomas Nichols, PhD
Dr. Nichols 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.
This event is part of the jointly organized weekly QLS lecture series aimed at training interdisciplinary researchers capable of harnessing the potential offered by big-data initiatives and neuroinformatics technologies. Partners: the Quantitative Life Sciences (QLS) Phd program, Ludmer Center Neuroinformatics & Mental Health, the Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM), and the McGill initiative in Computational Medicine (MiCM).