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Optimal representations of the genetic structure underlying complex neuroimaging phenotypes lie at the heart of our quest to discover the genetic code of the brain. Here, we suggest a strategy for achieving such a representation by decomposing the genetic covariance matrix of complex phenotypes into maximally heritable and genetically independent components. We show that such a representation can...
In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in and re-formulated using Lagrangian mechanics in. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first...
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