Available on ORBi since
28 September 2015
Doctoral thesis (Dissertations and theses)
Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data
Guillaume, Bryan 


Full Text
Author postprint (17.29 MB)

All documents in ORBi are protected by a user license.

Send to


Keywords :
Longitudianl neuroimaging data; Sandwich Estimator; Wild Bootstrap
Abstract :
[en] Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions. For example, the widely used SPM software package assumes spatially homogeneous longitudinal correlations while the FSL software package assumes Compound Symmetry, the state of all equal variances and equal correlations. While some new methods have been recently pro- posed to more accurately account for such data, these methods can be difficult to specify and are based on iterative algorithms that are generally slow and failure- prone. In this thesis, we propose and investigate the use of the Sandwich Estimator method which first estimates the parameters of interest with a (non-iterative) Ordinary Least Square model and, second, estimates variances/covariances with the “so-called” Sandwich Estimator (SwE) which accounts for the within-subject covariance structure existing in longitudinal data. We introduce the SwE method in its classic form, and review existing and propose new adjustments to improve its behaviour, specifically in small samples. We compare the SwE method to other popular methods, isolating the combination of SwE adjustments that provides valid and powerful inferences. While this result provides p-values at each voxel, it does not provide spatial inferences, e.g. voxel- or cluster-wise family-wise error-corrected p-values. For this, we investigate the use of the non-parametric inference approach called Wild Bootstrap. We again identify the set of procedures and adjustments that provide valid inferences. Finally, in the third and fourth projects, we investigate two ideas to improve the statistical power of the SwE method, by using a shrinkage estimator or a covariance spatial smoothing, respectively. For all the projects, in order to assess the methods, we use intensive Monte Carlo simulations in settings important for longitudinal neuroimaging studies and, for the first two projects, we also illustrate the methods by analysing a highly unbalanced longitudinal dataset obtained from the Alzheimer’s Disease Neuroimaging Initiative.
Research center :
GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging \u0026 Memory - ULiège
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Guillaume, Bryan ;  Université de Liège - ULiège > Form. doct. sc. ingé. (élec. & électro. - Bologne)
Language :
Title :
Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data
Defense date :
30 September 2015
Number of pages :
xxvi, 168
Institution :
ULiège - Université de Liège
Degree :
Doctor at Maastricht University and Docteur en sciences de l'ingénieur de l'Université de Liège
Promotor :
Phillips, Christophe  ;  Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
Nichols, Thomas
Matthews, Paul
President :
Van Steen, Kristel  ;  Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Biostatistics, biomedicine and bioinformatics
Jury member :
Formisano, Elia
Ridgway, Gerard
Rombouts, Serge
European Projects :
FP7 - 238593 - NEUROPHYSICS - Methods in Neuroimaging
Funders :
Initial Training Networks (FP7-PEOPLE-ITN-2008), Grant Agreement No. 238593 NEUROPHYSICS
CE - Commission Européenne


Number of views
648 (9 by ULiège)
Number of downloads
673 (3 by ULiège)


Similar publications

Contact ORBi