Modelling longitudinal structural change from serial MRI
Ridgway GR., Ashburner J., Penny W.
Temporal brain changes such as those in development, plasticity, ageing and neurodegeneration are best studied using longitudinal data. For example, the within-subject trajectory of the brain over the lifespan can differ from that inferred purely from cross-sectional data due to effects of birth-year (nutrition, etc.) in the latter. Serial imaging is also important for experimental studies that test the effects of treatments or training (e.g. Draganski et al., 2004). The most efficient methods for processing (e.g. spatially registering) longitudinal images capitalise on their withinsubject nature; however, doing so invokes a risk of bias from asymmetries or inconsistencies (Fox et al., 2011). Temporally-correlated data also require more sophisticated statistical analysis, and unbalanced data (with unequal numbers of time-points and/or uneven intervals) bring additional challenges (Bernal-Rusiel et al., in press). We have recently developed a longitudinal image registration framework (Ashburner & Ridgway, under review) that can precisely and consistently model structural change over time, allowing a set of images to be spatially warped to an iteratively-refined within-subject template with average shape and intensity (accounting for inhomogeneity or intensity non-uniformity present in MRI). Characteristics of these longitudinal warps (such as maps of their voxel-wise volume change, as illustrated in the figure) can then be spatially normalised to a between-subject template, for statistical parametric mapping analysis (or other statistical modelling, such as multivariate machine learning approaches). Methods for flexible and efficient spatio-temporal modelling of the processed data will be discussed. References Ashburner J, Ridgway GR (under review). Symmetric diffeomorphic modelling of longitudinal structural MRI. Bernal-Rusiel JL, Greve DN, Reuter M, Fischl B, Sabuncu MR (in press). Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models. NeuroImage Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004). Neuroplasticity: changes in grey matter induced by training. Nature 427(6972):311-2 Fox NC, Ridgway GR, Schott JM (2011). Algorithms, atrophy and Alzheimer's disease: Cautionary tales for clinical trials. NeuroImage 57(1):15-18