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


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
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    • "The T1 weighted (MDEFT) volumes from all participants were visually reviewed to exclude the presence of macroscopic artifacts. T1 weighted volumes were preprocessed for voxel-based morphometry (VBM) using the VBM8 toolbox implemented in SPM8 (Statistical Parametrical Mapping,, to produce a GM probability map [52, 53] in standard space (MNI coordinates) for every subject. In order to compensate for compression or expansion which might occur during warping of images to match the template, GM maps were " modulated " using the " non-linear only " option of VBM8, which adjusts every voxel's signal intensity by multiplying it by the amount of non-linear deformation only. "
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    ABSTRACT: Behavioral disorders and psychological symptoms (BPSD) in Alzheimer's disease (AD) are known to correlate with grey matter (GM) atrophy and, as shown recently, also with white matter (WM) damage. WM damage and its relationship with GM atrophy are reported in AD, reinforcing the interpretation of the AD pathology in light of a disconnection syndrome. It remains uncertain whether this disconnection might account also for different BPSD observable in AD. Here, we tested the hypothesis of different patterns of association between WM damage of the corpus callosum (CC) and GM atrophy in AD patients exhibiting one of the following BPSD clusters: Mood (i.e., anxiety and depression; ADmood), Frontal (i.e., dishinibition and elation; ADfrontal), and Psychotic (delusions and hallucinations; ADpsychotic) related symptoms, as well as AD patients without BPSD. Overall, this study brings to light the strict relationship between WM alterations in different parts of the CC and GM atrophy in AD patients exhibiting BPSD, supporting the hypothesis that such symptoms are likely to be caused by characteristic patterns of neurodegeneration of WM and GM, rather than being a reactive response to accumulation of cognitive disabilities, and should therefore be regarded as potential markers of diagnostic and prognostic value in AD.
    Full-text · Article · Jun 2016 · Journal of Alzheimer's disease: JAD
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    • "Gaser; by applying a Hidden Markov Random Field model (Ashburner and Friston, 2005). Secondly, we applied a so-called modulation to each cerebral partition image. "
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    ABSTRACT: Microstructural changes of White Matter (WM) associated with aging have been widely described through Diffusion Tensor Imaging (DTI) parameters. In parallel, White Matter Hyperintensities (WMH) as observed on a T2-MRI are extremely common in older individuals. However, few studies have investigated both phenomena conjointly. The present study investigates aging effects on DTI parameters in absence and in presence of WMH. Diffusion maps were constructed based on 21 directions DTI scans of young adults (n=19, mean age=33 SD=7.4) and two age-matched groups of older adults, one presenting low-level-WMH (n=20, mean age=78, SD= 3.2) and one presenting high-level-WMH (n=20, mean age=79, SD= 5.4). Older subjects with low-level-WMH presented modifications of DTI parameters in comparison to younger subjects, fitting with the DTI pattern classically described in aging, i.e. Fractional Anisotropy (FA) decrease/Radial Diffusivity (RD) increase. Furthermore, older subjects with high-level-WMH showed higher DTI modifications in Normal Appearing White Matter (NAWM) in comparison to those with low-level-WMH. Finally, in older subjects with high-level-WMH, FA or RD values of NAWM were associated with to WMH burden. Therefore, our findings suggest that DTI modifications and the presence of WMH would be two inter-dependent processes but occurring within different temporal windows. DTI changes would reflect the early phase of white matter changes and WMH would appear as a consequence of those changes.
    Full-text · Article · Feb 2016 · Frontiers in Aging Neuroscience
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    • " This processing procedure on the structural MR images included: (1) correcting for bias-field inhomogeneity; (2) spatially normalizing (affine-only transformation ); (3) segmenting into gray matter (GM), WM and cerebrospinal fluid (CSF) density maps by using the new-segment approach[Ashburner and Friston, 2005]; (4) warping the resultant WM density images to a Diffeomorphic Anatomical Registrations Through Exponentiated Lie Algebra (DARTEL) template using the high-dimensional DARTEL algorithm; (5) applying the modulation by multiplying the WM density map with the linear and nonlinear Figure 1. The classification schematic flow using the combined WM features. "
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    ABSTRACT: Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
    Full-text · Article · Jan 2016 · Human Brain Mapping
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