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Big Data Squared: Neuroimaging Genetics

Thomas NicolsGenetics and neuroimaging are two scientific disciplines where massive data plays an essential role. Over 1 million genetic markers are needed for a detailed view of a human individual, and high resolution Magnetic Resonance Imaging (MRI) depict the brain with over 1 million volume elements, or voxels. "Neuroimaging Genetics" is an emerging field that seeks to find genetic variants that explain why different people have different brain structure and brain function.

In this talk Dr Nichols will introduce this area and then present his work on statistical models and inference procedures that allow researchers to map the genetic influences throughout the brain. He will show specific applications with data from the Human Connectome Project; Dr Nichols is the imaging genetics expert on this project, which acquires and freely distributes cutting-edge functional and structural connectivity MRI data.

Thomas Nichols

Thomas Nichols is a Wellcome Trust Senior Research Fellow in Basic Biomedical Science, a Professor and the Head of Neuroimaging Statistics at the Institute for Digital Healthcare, holding a joint position between Warwick Manufacturing Group and the Department of Statistics. Before joining the University of Warwick he was the Director of Modelling & Genetics at the GlaxoSmithKline Clinical Imaging Centre at Hammersmith Hospital in London, where he worked on statistical methods for fMRI in the context of clinical trials, and integrating genetic data into brain image analyses.

Before coming to the UK he was an Associate Professor of Biostatistics at the University of Michigan, and in 2001 received his PhD in statistics from Carnegie Mellon University where he also trained in cognitive neuroscience. He has been active in the field of functional neuroimaging since 1992, when he worked at the University of Pittsburgh's PET Center as a programmer and statistician. Dr. Nichols' research focuses on modelling and inference of neuroimaging data, including PET, fMRI & M/EEG.