Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data

Bossa, Matías; Zacur, Ernesto; Olmos, Salvador Carrión

doi:10.1016/j.neuroimage.2010.12.078

Cited by 22 publications

(36 citation statements)

References 52 publications

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“…Although it rarely happens that atrophy of brain tissue causes only pose changes in adjacent structures without any local shape deformation, pose information is still very important in brain morphometry studies. In (Bossa et al, 2011), the statistical analysis of relative pose was presented to study brain atrophy associated pose changes of the subcortical nuclei in AD. Same method was applied in (Lao et al, 2013) to study pose changes of thalamus due to prematurity and in (Lao et al, 2014) to study pose changes of corpus callosum due to traumatic brain injury.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical analyses are then applied on the relative pose parameters. For details of the algorithm, please refer to (Bossa et al, 2011). Since our ventricular surface analysis gives indirect information about the atrophy of surrounding brain structures, integration of relative pose analysis in our system may help to better understand the abnormal growth of brain tissue adjacent to lateral ventricles.…”

Section: Discussionmentioning

confidence: 99%

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Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

et al. 2015

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Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer’s disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

et al. 2015

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“…Bossa et al [71] presented a method for the statistical analysis of the relative pose of subcortical nuclei. The framework of the analysis was a variety of approaches based on similarity transformations with Reimannian metrics.…”

Section: Methods Papersmentioning

confidence: 99%

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2013

Alzheimer's & Dementia

656

428

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The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and ...

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“…Let φ : SIMÔdÕ SOÔdÕ ¢ ST ÔdÕ be a smooth map, where ¢ denotes the direct product of the group sets [34] φ h SIM Ôs,θ,tÕ¨…”

Section: A Matrix Representation Ismentioning

confidence: 99%

Left-Invariant Riemannian Geodesics on Spatial Transformation Groups

Zacur¹,

Bossa²,

Olmos³

2014

SIAM J. Imaging Sci.

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Spatial transformations are mappings between locations of a d-dimensional space and are commonly used in computer vision and image analysis. Many of the spatial transformation sets have a group structure and can be represented by matrix groups. In the computer vision and image analysis fields there is a recent and growing interest in performing analyses on spatial transformations data. Differential and Riemannian geometry have been used as a framework to endow the set of spatial transformations with a metric space structure, allowing the extension of the standard analysis techniques defined on vector spaces. This paper presents a review of the concepts and an overview of approaches to computing Riemannian geodesics on spatial transformation groups. The paper is aimed at providing a bridge for researchers from computer vision and image analysis fields to fill in the gap between differential geometry and computer vision and imaging disciplines. Some application examples are shown to illustrate the use of invariant Riemannian geodesics, such as interpolation of spatial transformations and filtering of matrix-valued images.

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Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data

Cited by 22 publications

References 52 publications

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Left-Invariant Riemannian Geodesics on Spatial Transformation Groups

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