Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD

Singh, Nikhil; Wang, Angela Yee-Moon; Sankaranarayanan, Preethi; Fletcher, P. Thomas; Joshi, Sarang

doi:10.1007/978-3-642-33415-3_17

Cited by 22 publications

(26 citation statements)

References 8 publications

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“…Feature selection is more effective at the registration level between 5mm and 10mm since not only the pathological changes can be detected at this registration level but also the amount of noisy voxels (possible due to remaining inter-subject variability) is still large as shown in Figure 5. (4) In addition, when the classifier is trained on AD vs NC, the classification performance between PMCI and SMCI can be significantly improved, which is in accordance with results presented in previous studies [14], [51], [52]. This finding is encouraging because it is easier to obtain labeled training data from AD and NC subjects than from MCI subjects (MCI subjects need to be tracked for years to establish their training labels while the training labels of AD and NC subjects can be determined at baseline).…”

Section: Comparison With State-of-the-art Methodssupporting

confidence: 89%

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Tong

Gao

Ledig

et al. 2017

IEEE Trans. Biomed. Eng.

139

144

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Abstract-Objective: Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images.Methods: We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion.Results: Using the ADNI dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The classification AUC further increases to 84%-92% when age and cognitive measures are combined with the proposed grading biomarker.Conclusion: The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space; the removal of the normal aging effect; selection of discriminative voxels; the calculation of the grading biomarker using AD and normal control groups; the integration of sparse representation technique and the combination of cognitive measures.Significance: The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.

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Section: Comparison With State-of-the-art Methodssupporting

confidence: 89%

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Tong

Gao

Ledig

et al. 2017

IEEE Trans. Biomed. Eng.

139

144

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“…the two populations [35]. Commonly, in these methods the shape data are modelled on a Riemannian manifold and intrinsic coordinate-free manifold-based methods are used [8].…”

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confidence: 99%

Intrinsic Polynomials for Regression on Riemannian Manifolds

2014

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We develop a framework for polynomial regression on Riemannian manifolds. Unlike recently developed spline models on Riemannian manifolds, Riemannian polynomials offer the ability to model parametric polynomials of all integer orders, odd and even. An intrinsic adjoint method is employed to compute variations of the matching functional, and polynomial regression is accomplished using a gradient-based optimization scheme. We apply our polynomial regression framework in the context of shape analysis in Kendall shape space as well as in diffeomorphic landmark space. Our algorithm is shown to be particularly convenient in Riemannian manifolds with additional symmetry, such as Lie groups and homogeneous spaces with right or left invariant metrics. As a particularly important example, we also apply polynomial regression to time-series imaging data using a right invariant Sobolev metric on the diffeomorphism group. The results show that Riemannian polynomials provide a practical model for parametric curve regression, while offering increased flexibility over geodesics.

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“…In our experiments, these parameters are fixed to the standard values of α = 0.01, β = 0.01, and γ = 0.001. These fluid parameters have been used in previous studies in Davis et al (2007); Singh et al (2010, 2012) and are known to ensure sufficient smoothness of deformations fields for registration of MRI brain images. The parameter σ that controls the trade-off between the exactness of the match and smoothness regularity term in Equation (3) was also set a priori to the least possible value that ensured successful registration and also resulted in smooth and invertible deformation fields.…”

Section: Resultsmentioning

confidence: 99%

“…Singh et al (2010, 2012) used scalar deformation momenta to build models to explain covariance of shape and clinical data in the form of latent directions extracted in the two spaces but did not develop models summarizing functional relationship between anatomy and clinical variables. These models hence were not directly applicable for the prediction of continuous clinical response.…”

Section: Related Workmentioning

confidence: 99%

Quantifying anatomical shape variations in neurological disorders

Singh

Fletcher

Preston

et al. 2014

Medical Image Analysis

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We develop a multivariate analysis of brain anatomy to identify the relevant shape deformation patterns and quantify the shape changes that explain corresponding variations in clinical neuropsychological measures. We use kernel Partial Least Squares (PLS) and formulate a regression model in the tangent space of the manifold of diffeomorphisms characterized by deformation momenta. The scalar deformation momenta completely encode the diffeomorphic changes in anatomical shape. In this model, the clinical measures are the response variables, while the anatomical variability is treated as the independent variable. To better understand the “shape—clinical response” relationship, we also control for demographic confounders, such as age, gender, and years of education in our regression model. We evaluate the proposed methodology on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline structural MR imaging data and neuropsychological evaluation test scores. We demonstrate the ability of our model to quantify the anatomical deformations in units of clinical response. Our results also demonstrate that the proposed method is generic and generates reliable shape deformations both in terms of the extracted patterns and the amount of shape changes. We found that while the hippocampus and amygdala emerge as mainly responsible for changes in test scores for global measures of dementia and memory function, they are not a determinant factor for executive function. Another critical finding was the appearance of thalamus and putamen as most important regions that relate to executive function. These resulting anatomical regions were consistent with very high confidence irrespective of the size of the population used in the study. This data-driven global analysis of brain anatomy was able to reach similar conclusions as other studies in Alzheimer’s Disease based on predefined ROIs, together with the identification of other new patterns of deformation. The proposed methodology thus holds promise for discovering new patterns of shape changes in the human brain that could add to our understanding of disease progression in neurological disorders.

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Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD

Cited by 22 publications

References 8 publications

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Intrinsic Polynomials for Regression on Riemannian Manifolds

Quantifying anatomical shape variations in neurological disorders

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