Random Forests on Distance Matrices for Imaging Genetics Studies

The clinical pathology of neurological diseases and the imaging of the human brain are two areas of research that have largely developed along independent lines. It is only in the past few years that the usefulness of noninvasive imaging measurements of the human brain to the diagnosis and early prediction of neurological diseases been widely recognised (Albert et al., 2011; Sperling et al., 2011; Gray et al., 2013). In Alzheimer's Disease (AD), for instance, clinical guidance on the diagnosis of this most common of neurological degenerative disorders has recently been updated to incorporate neuroimaging markers alongside standard cognitive and behavioural tests (Albert et al., 2011; Sperling et al., 2011). The key to the improved characterisation of AD lies in the quantitative nature of the imaging measurements compared to the relatively subjective and imprecise nature of traditional clinical assessments. Imaging biomarkers of cerebral atrophy and of loss of connectivity between key regions in the brain are believed to be reliable indicators of AD and are particularly useful at early disease stages when standard cognitive assessments can be inconclusive. The utility of imaging phenotypes extends beyond diagnosis and prediction to the search for the underlying genetic factors behind neurological disorders (Stein et al., 2010). This comparatively more recent use of neuroimaging measurements in place of case-control labels in genetic association studies defines the emerging field of imaging genetics. The central premise here is that, should they exist, genetic associations to intermediate brain structure and brain function phenotypes are stronger than those with the categorical clinical disease statuses further down the etiological chain (Glahn et al., 2007). Again, the example of AD serves as a good illustration.