title: The importance of group-wise registration in tract based spatial statistics study of neurodegeneration: a simulation study in Alzheimer's disease.
abstract: Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a "ground truth" for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.
authors.affiliation: Dementia Research Centre, University College London Institute of Neurology, London, United Kingdom. s.keihaninejad@ucl.ac.uk
authors.forename: Shiva
authors.initials: S
authors.lastname: Keihaninejad
authors.forename: Natalie S
authors.initials: NS
authors.lastname: Ryan
authors.forename: Ian B
authors.initials: IB
authors.lastname: Malone
authors.forename: Marc
authors.initials: M
authors.lastname: Modat
authors.forename: David
authors.initials: D
authors.lastname: Cash
authors.forename: Gerard R
authors.initials: GR
authors.lastname: Ridgway
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.forename: Nick C
authors.initials: NC
authors.lastname: Fox
authors.forename: Sebastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D016880:Anisotropy; D001284:Atrophy / Q000473:pathology; D001921:Brain / Q000473:pathology; D016022:Case-Control Studies; D003198:Computer Simulation; D003710:Demography; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009410:Nerve Degeneration / Q000473:pathology*; D013223:Statistics as Topic