Multivariate models of inter-subject anatomical variability

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Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1 (1999), pp. 244–249 Morphological appearance manifolds in computational anatomy: groupwise registration and morphological analysis A consistent relationship between local white matter architecture and functional specialisation in medial frontal cortex Artificial Neural Networks. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470), volume 1 (1999) Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM New York, NY, USA (1992), pp. 144–152 Real-time volumetric deformable models for surgery simulation using finite elements and condensation Clinical Problem Solving and Diagnostic Decision Making: Selective Review of the Cognitive Literature Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM, Lect. Notes Comput. Sci., 3749 (2005), p. 1 Is multivariate analysis of PET data more revealing than the univariate approach? Evidence from a study of episodic memory retrieval Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging Variational inference for Bayesian mixtures of factor analysers, Adv. Neural. Inf. Process. Syst., 12 (2000), pp. 449–455 Functional–anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus Connectivity-based parcellation of human cortex using diffusion MRI: establishing reproducibility, validity and observer independence in BA 44/45 and SMA/pre-SMA Morphological classification of brains via high-dimensional shape transformations and machine learning methods Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM) Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. The Origin of Allometric Scaling Laws in Biology from Genomes to Ecosystems: Towards a Quantitative Unifying Theory of Biological structure and Organization (2005) Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia Source based morphometry using structural MRI phase images to identify sources of gray matter and white matter relative differences in schizophrenia versus controls IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, ICASSP 2008 (2008), pp. 533–536 This was the main conclusion of the New York Academy of Sciences “What Do We Want to See in Brain Imaging?” meeting (London, UK. 3–4 December, 2007). See for the accuracy with which handwritten digits can be recognized using various pattern recognition approaches. Cross-validation is exactly analogous to the hypothesis testing approach that is commonly accepted within the field, except that the hypotheses have been learned by a pattern recognition algorithm. Technically, these priors are “improper”. Probability densities must integrate to one, so a probability density allowing an infinite range of possible values must have zero probability everywhere. It is called the “Ugly Duckling Theorem” because a swan and a duckling are just as similar to each other as two swans. Source.

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