3D Characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, Los Angeles, CA 90095-1769, USA. NeuroImage
(Impact Factor: 6.36).
06/2008; 41(1):19-34. DOI: 10.1016/j.neuroimage.2008.02.010
Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.
Figures in this publication
Available from: Mingxia Liu
- "More recently, with the development of deformable image registration techniques, automatic spatial normalization proves to be a fundamental procedure in brain morphometric pattern analysis, which allows quantitative comparisons among different subjects in a common space. Within the spatial normalization framework, a large number of brain morphometric analysis methods are developed for automatic AD/MCI diagnosis, e.g., deformation-based morphometry (DBM)     , tensor-based morphometry (TBM)       , and voxel-based morphometry (VBM)      . "
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ABSTRACT: Multi-template based brain morphometric pattern analysis using magnetic resonance imaging (MRI) has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multi-view morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multi-template based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality the underlying data distribution is actually not pre-known. In this paper, we propose an inherent structure based multi-view leaning (ISML) method using multiple templates for AD/MCI classification. Specifically, we first extract multi-view feature representations for subjects using multiple selected templates, and then cluster subjects within a specific class into several sub-classes (i.e., clusters) in each view space. Then, we encode those sub-classes with unique codes by considering both their original class information and their own distribution information, followed by a multi-task feature selection model. Finally, we learn an ensemble of view-specific support vector machine (SVM) classifiers based on their respectively selected features in each view, and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multi-template based methods.
IEEE transactions on bio-medical engineering 11/2015; DOI:10.1109/TBME.2015.2496233 · 2.35 Impact Factor
Available from: Simon Duchesne
- "It incorporates local estimates of both tissue composition and deformation within a specific volume of interest (VOI) centered on the medial temporal lobes , providing a structural index related to disease progression . Such changes in tissue composition have been reported via voxel-based morphometry  and contrast studies , while volumetry  and tensor-based morphometry   reports have shown pathology-related deformations in specific brain areas. By incorporating both image features, we are able to capture different properties of the advancing pathological process and predict future clinical status for an individual subject. "
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ABSTRACT: Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer's disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer's Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique's ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.
International Journal of Alzheimer's Disease 08/2014; 2014:278096. DOI:10.1155/2014/278096
Available from: Clifford R Jack
- "The brain regions connected with the AL internal capsule are also among those that show AD-related brain atrophy (Hua, et al., 2008a). We tested associations of renal biomarkers with known cerebrospinal fluid biomarkers of AD, in a subset of ADNI subjects who had a lumbar puncture and had cerebrospinal fluid beta amyloid (n = 387) and tau (n = 379) measured. "
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ABSTRACT: Poor kidney function is associated with increased risk of cognitive decline and generalized brain atrophy. Chronic kidney disease impairs glomerular filtration rate, and this deterioration is indicated by elevated blood levels of kidney biomarkers such as creatinine and cystatin C. Here we hypothesized that impaired renal function would be associated with brain deficits in regions vulnerable to neurodegeneration. Using tensor-based morphometry, we related patterns of brain volumetric differences to creatinine, cystatin C levels, and glomerular filtration rate in a large cohort of 738 (mean age, 75.5 ± 6.8 years; 438 men, 300 women) elderly Caucasian subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Elevated kidney biomarkers were associated with volume deficits in the white matter region of the brain. All 3 renal parameters in our study showed significant associations consistently with a region that corresponds with the anterior limb of internal capsule, bilaterally. This is the first study to report a marked profile of structural alterations in the brain associated with elevated kidney biomarkers, helping us to explain the cognitive deficits.
Neurobiology of aging 11/2012; 34(4). DOI:10.1016/j.neurobiolaging.2012.10.022 · 5.01 Impact Factor
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