Article
Sparse bayesian learning for identifying imaging biomarkers in AD prediction.
Center for Neuroimaging, Department of Radiology and Imaging Sciences, USA.
Medical image computing and computerassisted intervention : MICCAI ... International Conference on Medical Image Computing and ComputerAssisted Intervention 01/2010; 13(Pt 3):6118. DOI: 10.1007/9783642157110_76 Source: PubMed

Article: Applying tensorbased morphometry to parametric surfaces can improve MRIbased disease diagnosis
[Show abstract] [Hide abstract]
ABSTRACT: Many methods have been proposed for computerassisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensorbased morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the socalled slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRIderived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 agematched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBMbased statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with imagebased classification.NeuroImage 02/2013; 74:209–230. · 6.13 Impact Factor 
Conference Paper: Structural brain network constrained neuroimaging marker identification for predicting cognitive functions.
[Show abstract] [Hide abstract]
ABSTRACT: Neuroimaging markers have been widely used to predict the cognitive functions relevant to the progression of Alzheimer's disease (AD). Most previous studies identify the imaging markers without considering the brain structural correlations between neuroimaging measures. However, many neuroimaging markers interrelate and work together to reveal the cognitive functions, such that these relevant markers should be selected together as the phenotypic markers. To solve this problem, in this paper, we propose a novel network constrained feature selection (NCFS) model to identify the neuroimaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities. Our new methods are evaluated by the MRI and AV45PET data from ADNIGO and ADNI2 (Alzheimer's Disease Neuroimaging Initiative). In all cognitive function prediction tasks, our new NCFS method outperforms other stateoftheart regression approaches. Meanwhile, we show that the new method can select the correlated imaging markers, which are ignored by the competing approaches.Information processing in medical imaging : proceedings of the ... conference; 01/2013  [Show abstract] [Hide abstract]
ABSTRACT: In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensorbased morphometry measures. We also combine the volumetric tensorbased morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.Neurobiology of Aging 08/2014; · 4.85 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.