Professor Thomas Nichols
Professor of Neuroimaging Statistics, Nuffield Department of Population Health
Dr. Nichols is the Professor of Neuroimaging Statistics and a Wellcome Trust Senior Research Fellow in Basic Biomedical Science. He is a statistician with a solitary focus on modelling and inference methods for brain imaging research. He has a unique background, with both industrial and academic experience, and diverse training including computer science, cognitive neuroscience and statistics. After serving on the faculty of University of Michigan's Department of Biostatistics he became the Director Modelling and Genetics at GlaxoSmithKline's Clinical Imaging Centre, London. He returned to academia in 2009 moving to the University of Warwick, taking a joint position between the Department of Statistics and the Warwick Manufacturing Group. He joined the BDI in 2017.
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
For a full list of publications please see my CV, my Google Scholar page, my NCBI Bibliography or ORCID profile; my research pages have publications in topical groups, or meet my students who do most of the work. My Neuroimaging Tips & Tricks blog has practical tips for neuroimaging researchers, and less practical stuff can be found on twitter.
Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants
McCracken C. et al, (2022), Nature Communications, 13
Association between brain similarity to severe mental illnesses and comorbid cerebral, physical, and cognitive impairments.
Ma Y. et al, (2022), Neuroimage
BLMM: Parallelised computing for big linear mixed models
Maullin-Sapey T. and Nichols TE., (2022), NeuroImage, 264, 119729 - 119729
Application of a convolutional neural network to the quality control of MRI defacing.
Delbarre DJ. et al, (2022), Comput Biol Med, 151
Brain-wide versus genome-wide vulnerability biomarkers for severe mental illnesses.
Kochunov P. et al, (2022), Human brain mapping