Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants

The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
NeuroImage (Impact Factor: 6.36). 07/2009; 46(2):486-99. DOI: 10.1016/j.neuroimage.2009.01.002
Source: PubMed


The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.

Download full-text


Available from: Kenichi Oishi,
  • Source
    • "A drawback of ABA is that it requires pre-defined structural definitions . In this study, we used a parcellation based on classical anatomical definitions (Mori, 2008; Oishi, 2009). There are multiple other ways to parcellate the brain based on different criteria, for example, vascular, cytoarchitectonic, or functional maps. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.
    Clinical neuroimaging 01/2015; 46. DOI:10.1016/j.nicl.2015.01.008 · 2.53 Impact Factor
  • Source
    • "Although cross-sectional group comparisons have identified widespread WM abnormalities in AD, the above studies failed to identify reduced volume or FA in the fornix, except for a study that used multivariate analysis (Teipel et al., 2007). The main explanation is probably inaccuracy in image normalization: the accuracy of automated non-linear registration used in these studies was limited for thin structures like the fornix, in which only a few pixels of misregistration cause significant loss of sensitivity to detect differences between groups (Oishi et al., 2009). A common approach to solve this problem is to apply Tract-Based Spatial Statistics (TBSS; Smith et al., 2006), in which WM tracts are “skeletonized” to summarize the anatomy of WM structures, from which statistics are calculated. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Alzheimer's disease (AD) is the most common form of neurodegenerative dementia. Researchers have long been focused on the cortical pathology of AD, since the most important pathologic features are the senile plaques found in the cortex, and the neurofibrillary tangles and neuronal loss that begin in the entorhinal cortex and the hippocampus. In addition to these gray matter (GM) structures, histopathological studies indicate that the white matter (WM) is also a good target for both the early diagnosis of AD and for monitoring disease progression. The fornix is a WM bundle that constitutes a core element of the limbic circuits, and is one of the most important anatomical structures related to memory. Functional and anatomical features of the fornix have naturally captured researchers' attention as possible diagnostic and prognostic markers of AD. Indeed, neurodegeneration of the fornix has been histologically observed in AD, and growing evidence indicates that the alterations seen in the fornix are potentially a good marker to predict future conversion from mild cognitive impairment (MCI) to AD, and even from cognitively normal individuals to AD. The degree of alteration is correlated with the degree of memory impairment, indicating the potential for the use of the fornix as a functional marker. Moreover, there have been attempts to stimulate the fornix using deep brain stimulation (DBS) to augment cognitive function in AD, and ongoing research has suggested positive effects of DBS on brain glucose metabolism in AD patients. On the other hand, disease specificity for fornix degeneration, methodologies to evaluate fornix degeneration, and the clinical significance of the fornix DBS, especially for the long-term impact on the quality of life, are mostly unknown and need to be elucidated.
    Frontiers in Aging Neuroscience 09/2014; 6:241. DOI:10.3389/fnagi.2014.00241 · 4.00 Impact Factor
  • Source
    • "Inversely, this parcellation map was warped to the original MRI data, thus automatically parcelling each of our subject's brains. For details, please read our previous publications (Faria et al., 2010, 2011, 2012; Oishi et al., 2009). Note that the aim of mapping each brain image to a common template was to be able to group voxels into structures pre-defined in this template, therefore reducing the dimensionality of the data and improving the signal-to-noise ratio. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Three variants of primary progressive aphasia (PPA), distinguished by language performance and supportive patterns of atrophy on imaging, have different clinical courses and the prognoses for specific functions. For example, semantic variant PPA alone is distinguished by impaired word comprehension. However, sometimes individuals with high education show normal performance on word-comprehension tests early on, making classification difficult. Furthermore, as the condition progresses, individuals with other variants develop word-comprehension deficits and other behavioural symptoms, making distinctions between variants less clear. Longitudinal brain imaging allows identification of specific areas of atrophy in individual patients, which identifies the location of disease in each patient.
    Aphasiology 08/2014; 28(8-9):948-963. DOI:10.1080/02687038.2014.911241 · 1.53 Impact Factor
Show more