Unified Segmentation

Wellcome Department of Imaging Neuroscience, 12 Queen Square, London, WC1N 3BG, UK.
NeuroImage (Impact Factor: 6.36). 08/2005; 26(3):839-51. DOI: 10.1016/j.neuroimage.2005.02.018
Source: PubMed

ABSTRACT A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

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Available from: Karl J Friston, Aug 21, 2015
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    • "Preprocessing steps included bias-field correction and segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Segmented images were registered to standard Montreal Neurological Institute (MNI) space using the high-dimensional Dartel approach (Ashburner and Friston, 2005 "
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    ABSTRACT: There is increasing research interest in the structural and functional brain correlates underlying creative potential. Recent investigations found that interindividual differences in creative potential relate to volumetric differences in brain regions belonging to the default mode network, such as the precuneus. Yet, the complex interplay between creative potential, intelligence, and personality traits and their respective neural bases are still under debate. We investigated regional gray matter volume (rGMV) differences that can be associated with creative potential in a heterogeneous sample of N = 135 individuals using voxel-based morphometry (VBM). By means of latent variable modeling and consideration of recent psychometric advancements in creativity research, we sought to disentangle the effects of ideational originality and fluency as two independent indicators of creative potential. Intelligence and openness to experience were considered as common covariates of creative potential. The results confirmed and extended previous research: rGMV in the precuneus was associated with ideational originality, but not with ideational fluency. In addition, we found ideational originality to be correlated with rGMV in the caudate nucleus. The results indicate that the ability to produce original ideas is tied to default-mode as well as dopaminergic structures. These structural brain correlates of ideational originality were apparent throughout the whole range of intellectual ability and thus not moderated by intelligence. In contrast, structural correlates of ideational flueny, a quantitative marker of creative potential, were observed only in lower intelligent individuals in the cuneus / lingual gyrus.
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    • "The T1-weighted scans were partitioned into different tissue 31 classes-grey matter, white matter and non-brain voxels (cerebrospinal fluid, skull) -based on 32 separate tissue probability maps for each tissue class using the " new segmentation " approach in 33 SPM8 (Ashburner, 2007). In order to compare brains of different subjects, the resulting segments 34 were normalized to a population template generated from the complete dataset using a 35 diffeomorphic registration algorithm (Ashburner and Friston, 2005). This high-dimensional non- 36 linear warping algorithm selects conserved features, which are informative for registration, thus 37 minimizing structural variation among subjects and providing optimal inter-subject registration. "
    • "This included an automated quality insurance protocol, which all scans (in addition to being checked visually for artefacts) passed. All T1-weighted images were corrected for bias-field inhomogeneities , then spatially normalised and segmented into grey (GM), white matter (WM), and cerebrospinal fluid (CSF) within the same generative model (Ashburner and Friston, 2005). As described previously (Gaser, 2009), the segmentation procedure was further extended by accounting for partial volume effects (Tohka et al., 2004), applying adaptive maximum a posteriori estimations (Rajapakse et al., 1997), and using a hidden Markov Random Field model (Cuadra et al., 2005). "
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