2016 - present: Associate Editor, Canadian Journal of Statistics
2013: Guest Co-Editor, Statistical Methods in Medical Research, Special Issue on Statistics for Imaging
This special issue contains five papers focussed on
neuroimaging statistics: Bayesian nonparametric modeling of fMRI data;
brain activity with positron-emission tomography using a Bayesian spatial model; Bayesian computation
for neuroimaging within the context of the electromagnetic inverse problem; spectral analysis of resting-state
magnetoencephalography data with space-varying regression; constructing
shape features and detecting hippocampal shape changes in early
Alzheimer’s using support vector machines.
- Nie, Y., *Yasmin, L., *Song, Y., Scarapicchia, V., Gawryluk,
J., Wang, L., Cao, J., Nathoo, F.S. Spectral Dynamic Causal Modelling
of Resting-State fMRI: Relating Effective Brain Connectivity in the
Default Mode Network to Genetics. Submitted for publication.
- *Song, Y., Ge, S., Cao, J., Wang, L., Nathoo, F.S. A Bayesian
Spatial Model for Imaging Genetics. Submitted for publication.
- *Song, Y., Nathoo, F.S., Babul A. (2019). A Potts-Mixture Spatiotemporal Joint Model for Combined MEG and EEG Data. Canadian Journal of Statistics, accepted for publication.
- Nathoo, F.S.,
Kong, L., Zhu, H. (2018). A Review of Statistical Methods in Imaging Genetics. Canadian Journal of Statistics, DOI: 10.1002/cjs.11487.
- Nathoo, F.S., *Kilshaw, R.E., Masson, M.E.J. (2018). A
Better (Bayesian) Interval Estimate for Within-Subject
Designs. Journal of Mathematical Psychology, DOI: https://doi.org/10.1016/j.jmp.2018.07.005.
- *Teng, M., Johnson, T.D., Nathoo, F.S. (2018). Time Series Analysis of fMRI Data: Spatial Modeling and Bayesian Computation. Statistics in Medicine, DOI: 10.1002/sim.7680.
- *Greenlaw, K., Szefer, E., Graham, J., Lesperance, M.L., Nathoo, F.S. (2017). A Bayesian Group Sparse Mutli-Task Regression Model for Imaging Genetics. Bioinformatics, DOI: 10.1093/bioinformatics/btx215. Model derviations are contained in the supplementary material.
- Szefer, E., Lu, D., Nathoo, F., Beg, M. F., Graham, J. (2017). Multivariate
association between single-nucleotide polymorphisms in Alzgene linkage
regions and structural changes in the brain: discovery, refinement and
validation. Statistical Applications in Genetics and Molecular Biology, 16(5-6), 367-386.
- Nathoo, F.S. and Masson, E.J. M. (2015), Bayesian Alternatives to Null-Hypothesis Significance Testing for Repeated Measures Designs. Journal of Mathematical Psychology, http://dx.doi.org/10.1016/j.jmp.2015.03.003.
- Nathoo, F.S., Babul, A., Moiseev, A. Virji-Babul, N., Beg, M.F. (2014). A Variational Bayes Spatiotemporal Model for Electromagnetic Brain Mapping. Biometrics, 70(1), pp.132-143. Model derviations are contained in the supplementary material.
- Robertson, C., Long, J.A., Nathoo, F.S., Nelson, T.A., and Plouffe, C.C.F. (2014). Assessing quality of spatial models using the structural similarity index and posterior predictive checks. Geographical Analysis, 46, 53-74.
- Nathoo, F.S., and Ghosh,
P. (2012). "Skew-Elliptical Spatial Random Effect Modeling for Areal
Data with Application to Mapping Health Utilization Rates." Statistics in Medicine, DOI: 10.1002/sim.5504.
- Robertson, C., Sawford, K., Gunawardena, S., Nelson, T.A., Nathoo, F.S., Stephen, C. (2011). "A hidden Markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance." PLoS ONE 6(9): e24833. doi:10.1371/journal.pone.0024833.
- Ghosh, P., Nathoo F.S., Gonenn, M., and Tiwari, R.C. (2010). "Assessing noninferiority in a three-arm trial using the Bayesian approach." Statistics in Medicine, 30, 1795-1808.
- Nathoo, F.S. (2010). "Joint spatial modeling of recurrent infection and growth with processes under intermittent observation." Biometrics, 66, 336-346. Model derivations are contained in the supplementary material.
- Nathoo, F.S. and Dean, C.B. (2008). "Spatial multi-state transitional models for longitudinal event data." Biometrics, 64, 271-279. Model derivations are contained in the supplementary material.
- Nathoo, F.S. and Dean, C.B. (2007). "A mixed mover-stayer model for spatio-temporal two-state processes." Biometrics, 63, 881-891. Model derivations are contained in the supplementary material.
- Dean, C.B., Nathoo, F.S. and Nielson, J.D. (2007). "Spatial and mixture models for recurrent event processes." Enivronmetrics, 18, 713-725.
Trainees indicated with '*'.
- R package 'bgsmtr' for Bayesian regression analysis of imaging genetics data.
This R package implements the methodology in Greenlaw et al. (2017) and
Song et al. (2019).
The package has options for both spatial and non-spatial error
covariance models and as options for computational implementation based
on both MCMC and mean-field variational Bayes. Bayesian false
discovery rate procedures are also implemented in the latest version of
- R software
'Pottsmix' for fitting the latent Gaussian spatiotemporal mixture model
for solving the electromagnetic inverse problem based on combined MEG
and EEG data. This implements the iterated
conditional modes algorithm for simultaneous point estimation and model
selection developed in Song, Nathoo and Babul (2019).
Huber has written a nice Shiny App implementing the Bayes factor
approximations for repeated measures designs developed in Nathoo and
- Matlab codes for fitting the spatial spike-and-slab model for solving the
electromagnetic inverse problem based on combined MEG (or EEG) and fMRI
data. The software implements the mean-field variational Bayes algorithm developed in Nathoo et al. (2014).
- C++ Codes
for Hamiltonian Monte Carlo, mean-field variational Bayes and integrated nested
Laplace approximation approaches for fitting Log-Gaussian Cox processes based on
Teng, Nathoo and Johnson (2017).
software and examples for mixed effects repeated measures designs with
binary response developed in Song, Nathoo and Masson (2017).
- Python codes for feature selection methods (t-test, lasso, PCA, stacked autoencoder) discussed in Shi and Nathoo (2018).
Recent Lab News:
- Joint Statistical Meetings 2019
Invited Session Proposal ‘Making an Impact in Neuroscience: Advances in
Statistical Methods for Brain Imaging’ has been accepted for the
invited program. Nathoo will be a discussant in this session.
- Nathoo has been elected to the Board of Directors of the Canadian Statistical Sciences Institute for a three year term, 2018 - 2021.
- Nathoo is a Member of the NSERC Mathematics and Statistics Evaluation Group (EG 1508), 2016-2019.
- Former lab member Keelin
Greenlaw won a CANSSI-sponsored Best Paper Award at the 2015
Statistical Society of Canada Student Conference.
(Hong) Li wins the TIES-WILEY Best Student Poster Award at the 2008 International Environmetrics Society Annual
Conference for her work in modelling mountain pine beetle infestation.
Students (two teams) win both of the SSC Case Studies in Data Analysis Competitions
at the 2009 Statistical Society of Canada Conference.
I am currently recruiting PhD and MSc students to work on a number of projects
involving Bayesian methods and neuroimaging data. Potential
applicants should have very strong programming skills (we emphasize R
and Matlab but some students use Python), a good undergraduate math
background. Interested candidates should contact Farouk Nathoo
(email@example.com), send me your undergraduate and graduate transcripts,
your publications if any, and research interests. A formal application to the Department of Mathematics and Statistics, University of Victoria is required in all cases.
Current Lab Members:
Yasmin (MSc Student)
- Research Interests: Imaging genetics and fMRI data analysis
- Yin Song (PhD Student)
- Research Interests: Statistical computing and programming, MEG/EEG inverse problem, Bayesian methods and variational Bayes
Yin presenting an e-poster at JSM 2018.
- Shan Shi
- Research Interests: Machine Learning for neuroimaging and genetic data
- Eugene Opoku (PhD Student)
- Research Interests: Hidden Markov random fields and associated optimization algorithms
- Li Xing (Postdoctoral
- Research Interests: Analysis of longitudinal imaging genetics data
Li presenting an e-poster at JSM 2018.
In Fall 2018 I am teaching MATH/STAT 452/552 (Stochastic
Processes/Applied Stochastic Models; course outline
) and STAT 460/560 (Bayesian
Statistics; course outline
). Course material will be distributed in class.
I am a co-leader of the Canadian Statistical Sciences Institute Collaborative Research Team: "Joint Analysis of Neuroimaging Data: High-dimensional Problems, Spatio-Temporal Models and Computation" 2016 - 2019.
- An article written by CANSSI descrbiing recent team progress is here.
- A Times Colonist News article on the team is here.
- Information on the team from the CANSSI website is here.
- A Medical Xpress article on the team is here.
- The other team co-leader is Linglong Kong from the Mathematics and Statistics Department at University of Alberta.
- The scientific program (including the abstracts from student
talks) for our 2016 team meeting held at the University of Victoria is
- Slides summarizing team achievements are here.
- Member of
the Organizing Committee, The Seventh International Workshop on the
Perspectives on High-Dimensional Data Analysis, CMAT, Guanajuato,
Mexico, June 2017.
- Member of the Local Organizing Committee, Organization for
Human Brain Mapping (OHBM) Annual Meeting, Vancouver, British
Columbia, June 2017.
- Member of the Organizing Committee, The Sixth International
Workshop on the Perspectives on High-Dimensional Data Analysis,
Fields Institute, May 2016.
- Member of the Organizing Committee, Mathematical and Statistical Challenges in Neuroimaging Data Analysis, BIRS, January 2016.
- Program Co-Chair, Workshop on Applied Topology and High-Dimensional Data Analysis, University of Victoria, August 2015.
- Member of the Organizing Committee, International Workshop on the Perspectives on High-dimensional Data Analysis III, Vancouver, May 2013.
Co-Chair, GEOMED 2011:
GeoMedical Systems and Spatial Statistics International Conference,
Victoria, October 2011.