Reference : Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/186284
Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data
English
Guillaume, Bryan mailto [Université de Liège - ULiège > > > Form. doct. sc. ingé. (élec. & électro. - Bologne)]
30-Sep-2015
Université de Liège, ​Liège, ​​Belgique
Doctor at Maastricht University and Docteur en sciences de l'ingénieur de l'Université de Liège
xxvi, 168
Phillips, Christophe mailto
Nichols, Thomas mailto
Matthews, Paul mailto
Van Steen, Kristel mailto
Formisano, Elia mailto
Ridgway, Gerard mailto
Rombouts, Serge mailto
[en] Longitudianl neuroimaging data ; Sandwich Estimator ; Wild Bootstrap
[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.
Centre de Recherches du Cyclotron - CRC
Initial Training Networks (FP7-PEOPLE-ITN-2008), Grant Agreement No. 238593 NEUROPHYSICS
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/186284
FP7 ; 238593 - NEUROPHYSICS - Methods in Neuroimaging

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