I have moved! Thomas Nichols is now at the University of Oxford; this is an iterim web page; see also my Oxford home page. Thomas Nichols is a Wellcome Trust Senior Research Fellow in Basic Biomedical Science and the Professor of Neuroimaging Statistics at the Oxford Big Data Institute. He previously was a Professor in the Warwick Department of Statistics and WMG, and before that 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 Ph.D. 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. 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.

News

• Presentations from OHBM 2016! Posters & Talks
• With my Human Connectome Project collaborators, we found a single axis of positive and negative life factors correlated with brain connectivity (Smith et al., Nature Neuroscience 2015). Or, as the Daily Mail put it "Intelligent people's brains are wired differently"; I was interviewed for a Yahoo! News' piece titled "Happy brains are wired that way."
• Congratulations to my most recent PhD, Bryan Guillaume, who defended his dissertation "Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data" as part of a unique Maastricht University / University of Liège degree. His sandwich estimator method is available as the SwE SPM toolbox, and a FSL tool is under development.
• Neuroimaging meta-analysis with Bayesian spatial models to predict emotional class of each study, getting 66% accuracy on a 5-way classification problem; an analgous Naive Bayes classifier obtained 33% accuracy (20% is chance). Wager et al. A bayesian model of category-specific emotional brain responses (2015), PLOS Computational Biology 11(4):e1004066.
• Massively Expedited Genome-wide Heritability Analysis, or MEGHA, provides blazlingly fast estimates of heritabiltiy with unrelated individuals, perfect for mapping heritabilty in high-dimensional brain imaging and connectomic data. Ge et al. (2015), Proc. National Academy of Sciences of the USA, software.
• Call for articles, for Special Issue in NeuroImaging: "Sharing the wealth: Brain Imaging Repositories in 2015". I'm co-guest-editing this issue with Jessica Turner & Simon Eickhoff, to highly the importance of open data in neuroimaging.
• Life in a Shell - Linux tips for scientific computing: PDF | Sandbox.zip
• Mega or Meta? Comparison of "mega-analysis" methods (joint modelling all data, a.k.a. "individual patient data" meta-analysis) with usual meta-analysis methods for inference on heritability of white matter tract data: Kochunov et al. (2014).
• See our posters presented at the MICCAI and ACTRIMS-ECTRIMS Meetings on Multiple Sclerosis lesion modelling.
• I've been named on Thompson-Reuter's Highly Cited Researchers 2014 list! The list is based on how frequently my work was cited in the period 2002-2012. See press release and full report for more details.
• Random graph mixture models for brain connectivity, validated on C. elegans: Stochastic Blockmodeling of the Modules and Core of the Caenorhabditis elegans Connectome by Pavlovic, Vértes, Bullmore, Schafer, & Nichols, PLoS One 9(7):e97584.
• Spatial Bayesian regression modelling of MS lesions: Analysis of Multiple Sclerosis Lesions via Spatial Varying Coefficients by Ge, Müller-Lenke, Bendfeldt, Nichols, & Johnson, The Annals of Applied Statistics 8(2), 1095–1118.
• Statistics and Science: A Report of the London Workshop on the Future of the Statistical Sciences, an interesting white paper produced by a joint committee of six statistical socieities around the globe, in commeration of the 2013 International Year of Statistics.
• Warwick Medical Imaging Network (W-MIN) - A group I lead with Joanna Collingwood that hosts seminars and tutorials on all aspects of medical imaging (above the neck and below; in humans and other animals)
• "Inside-Out" is a new EPSRC-funded grant to develop the statistical methods for additive manufacturing (aka 3D printing). This is a joint project between Statistics & WMG, led by Wilfrid Kendall, it funds investigators and post-docs in Statistics & WMG. See the project pages in Stats and WMG for more.
• Threshold-Free Cluster Enhancement and Bayes Net models used to quantify gray matter changes with B-vitamin in elderly at risk for dementia: Douaud et al. (2013). PNAS, 110(23), 9523–8.
• Recent blog posts on FWHM & RESEL details for SPM and FSL, Standardizing DVARS and What are the units in plots in SPM?.

Recent Publications:

• "Alternative-based thresholding with application to presurgical fMRI." Cognitive, Affective, & Behavioral Neuroscience. doi:10.3758/s13415-013-0185-3, a new thresholding method for presurgical fMRI, or whenever one needs to assert "no activation".
• "Genetics of the Connectome". NeuroImage 80, 475–488., concise overview of the role Imaging Genetics is playing with the Connectome.
• "Preventing Alzheimer’s disease-related gray matter atrophy by B-vitamin treatment." Proceedings of the National Academy of Sciences of the United States of America, 110(23), 9523–8, a voxel-wise analysis of a clinical trial for dimentia.
• "Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures", NeuroImage 63(2):858–873, fast, accurate and powerful inference for whole-brain, whole-genome imaging genetics studies.
• "A Bayesian non-parametric Potts model with application to pre-surgical FMRI data", Stat Methods Med Res, a spatial mixture model for thresholding unsmoothed data, useful for clinical fMRI.

Recent Presentations:

• Posters and talk slides from my group and collaborators at the Organization for Human Brain Mapping 2014 conference, 8-12 June.
• Slides from the "GlaxoSmithKline - Neurophysics Workshop on Skeptical Neuroimaging", a 1-day event that dug into the thorny issues of reproducibility, data-sharing and meta-analysis in brain imaging.

Collaborations

• I am part of the Washington University - University of Minnesota Human Connectome Project, an ambitious international consortium focused on mapping the structural and functional connections in the human brain. I am working on the imaging genetics component of this study, which will image twins and their siblings, allowing the estimation of heritability of connectivity measures.

Recently Released

• SnPM13, the Statistical Nonparametric Mapping toolbox for SPM has been released, 6 March, 2013. See the SnPM page for more.

Neuroimaging Tips & Tricks

• Visit my blog on Neuroimaging Tips & Tricks, an extension of the John's (SPM software) Gems pages I started almost 10 years ago.

Past Teaching:

• Term 1 2012/13 Applied Biostatistics, ST416 (I'm teaching the 1st part of this 3-module course)
• Term 2 2012/13 Probabilistic and Statistical Inference, CO902 (Complexity MSc course)

Neuroimaging
Statistics

Contact Info

Room D0.03
Deptment of Statistics
University of Warwick
Coventry
CV4 7AL
United Kingdom

Tel: +44(0)24 761 51086
Email: t.e.nichols 'at' warwick.ac.uk
Web: http://nisox.org
Blog: NISOx blog

Handbook of fMRI Data Analysis by Russ Poldrack, Thomas Nichols and Jeanette Mumford