Unbiased Robust Template Estimation for Longitudinal Analysis in FreeSurfer.
M. Reuter, H.D. Rosas, B. Fischl.
Human Brain Mapping 2010, Barcelona. Abstract / Poster.
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Compared with cross-sectional studies, a longitudinal design can significantly reduce the confounding effect of inter-individual morphological variability by using each subject as his or her own control. As a result, longitudinal imaging studies are increasing in popularity in various aspects of neuroscience. Changes in gray matter that makes up the cortical sheet are for example manifested in aging, Alzheimer's disease, Huntington's disease, multiple sclerosis and schizophrenia. In vivo cortical thickness measures could be useful as marker of disease progression or onset. Longitudinal imaging-based biomarkers are thus of great potential utility in evaluating the efficiency of disease-modifying therapies. For these reasons, developing more robust and reliable measures of cortical, subcortical and white matter atrophy may have a profound clinical impact. The current methods that utilize cross-sectional approaches, in which images are processed individually introduce the natural variability of the brain as a confound. We sought to develop and validate a longitudinal approach that takes advantage of intra-subject longitudinal acquired scans to improve the sensitivity and reliability of automatic neuro-imaging morphometic measures. hide |