Abstract #2899

Integration of Model Information Into Model-Free Multivariate Analyses of Structural and Diffusion MRI Data

Stephen Smith¹, Achim Gass², Andreas U. Monsch³, Anil Rao⁴, Brandon Whitcher⁴, Paul M. Matthews⁴, Christian Beckmann^1,5

¹Oxford University FMRIB Centre, Oxford, Oxon, UK; ²Depts. Neurology and Neuroradiology, University Hospital Basel, Switzerland; ³Memory Clinic Basel, Switzerland; ⁴GlaxoSmithKline, Clinical Imaging Centre, London, UK; ⁵Clinical Neuroscience Department, Imperial College London, UK, UK

Model-based analysis methods are generally reliable in identifying expected responses, aiding interpretability of results. Conversely, model-free methods are able to find surprising effects in the data, or separate out structured confound processes from signals of interest. Little work has been carried out to combine the best aspects of the different approaches. We present initial results from inserting model information into a multivariate model-free approach and compare this approach to a GLM analysis as well as to PLS and CVA. We find that approaches containing both model-based and data-driven aspects are almost always more interpretable than rigidly enforcing model structure.