7 citations · 16 across the 16 of their papers we have counts for
19 papers
Shape-Based Inductive Bias for Glioma Grading from Tumor Contours
Puneet Velidi, Michelle F. Miranda, Farouk Nathoo +2
Glioma grading from tumor contours is often treated as a pixel problem even when the signal of interest is shape. We align closed contours with a functional shape-alignment framewo…
Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Puneet Velidi, Zhengxiao Wei, Shreena Nisha Kalaria +4
Concerns about the misuse and misinterpretation of p-values and statistical significance have motivated alternatives for quantifying evidence. We define a generalized form of Jeffr…
Survival Analysis with Discrete Biomarkers Under a Semiparametric Bayesian Conditional Poisson Model
Aijun Yang, Phineas T. Hamilton, Brad H. Nelson +3
Discrete biomarkers derived as cell densities or counts from tissue microarrays and immunostaining are widely used to study immune signatures in relation to survival outcomes in ca…
Strong Consistency of the SIMEX Estimator in Linear Regression with a Conditionally Poisson Covariate
Aijun Yang, Mary Lesperance, Farouk S. Nathoo
This paper considers estimation for linear regression analysis with covariate measurement error arising from Poisson surrogates. We consider cases where covariates follow a conditi…
A Wavelet-Based Framework for Mapping Long Memory in Resting-State fMRI: Age-Related Changes in the Hippocampus from the ADHD-200 Dataset
Yasaman Shahhosseini, Cédric Beaulac, Farouk S. Nathoo +1
Functional magnetic resonance imaging (fMRI) time series are known to exhibit long-range temporal dependencies that challenge traditional modeling approaches. In this study, we pro…
POI-SIMEX for Conditionally Poisson Distributed Biomarkers from Tissue Histology
Aijun Yang, Phineas T. Hamilton, Brad H. Nelson +3
Covariate measurement error in regression analysis is an important issue that has been studied extensively under the classical additive and the Berkson error models. Here, we consi…
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
Cédric Beaulac, Sidi Wu, Erin Gibson +5
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem…
Spectral Dynamic Causal Modelling of Resting-State fMRI: Relating Effective Brain Connectivity in the Default Mode Network to Genetics
Yunlong Nie, Eugene Opoku, Laila Yasmin +8
We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's dise…
A Bayesian Spatial Model for Imaging Genetics
Yin Song, Shufei Ge, Jiguo Cao +2
We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model…
Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging
Shan Shi, Farouk Nathoo
Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely en…
A Better (Bayesian) Interval Estimate for Within-Subject Designs
Farouk S. Nathoo, Robyn E. Kilshaw, Michael E. J. Masson
We develop a Bayesian highest-density interval (HDI) for use in within-subject designs. This credible interval is based on a standard noninformative prior and a modified posterior…
Bayesian Analysis of fMRI data with Spatially-Varying Autoregressive Orders
Ming Teng, Farouk S. Nathoo, Timothy D. Johnson
Statistical modeling of fMRI data is challenging as the data are both spatially and temporally correlated. Spatially, measurements are taken at thousands of contiguous regions, cal…
A Potts-Mixture Spatiotemporal Joint Model for Combined MEG and EEG Data
Yin Song, Farouk S. Nathoo, Arif Babul
We develop a new methodology for determining the location and dynamics of brain activity from combined magnetoencephalography (MEG) and electroencephalography (EEG) data. The resul…
A Review of Statistical Methods in Imaging Genetics
Farouk S. Nathoo, Linglong Kong, Hongtu Zhu
With the rapid growth of modern technology, many large-scale biomedical studies have been/are being/will be conducted to collect massive datasets with large volumes of multi-modali…
Bayesian Computation for Log-Gaussian Cox Processes--A Comparative Analysis of Methods
Ming Teng, Farouk S. Nathoo, Timothy D. Johnson
The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point patterns. Fitting this model is difficult because of its doubly-stochastic property, i.e., i…
Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation
Ming Teng, Timothy Johnson, Farouk Nathoo
Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. The neuroimaging community has embraced mean-field variational Bayes (VB) approx…
A Bayesian Group Sparse Multi-Task Regression Model for Imaging Genetics
Keelin Greenlaw, Elena Szefer, Jinko Graham +2
Motivation: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. W…
Regularization Parameter Selection for a Bayesian Multi-Level Group Lasso Regression Model with Application to Imaging Genomics
Farouk S. Nathoo, Keelin Greenlaw, Mary Lesperance
We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model…
High-Dimensional Classification for Brain Decoding
Nicole Croteau, Farouk S. Nathoo, Jiguo Cao +1
Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the c…