ArticleNeuroImage2013
Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis.
Article in NeuroImage, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Postmortem neuropathologies are associated with retrospective tensor-based morphometry findings in Alzheimer's dementia continuum.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- MRI-Driven Longitudinal Studies of Hippocampal Alterations During the Initial Cognitive Decline.IEEE journal of translational engineering in health and medicine · 2025Article
- Surface-based morphometry of the corpus callosum in young children of ages 1-5.Human brain mapping · 2024Article
- Fine-grained features characterize hippocampal and amygdaloid change pattern in Parkinson's disease and discriminate cognitive-deficit subtype.CNS neuroscience & therapeutics · 2024Article
- Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics.CNS neuroscience & therapeutics · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.Journal of Alzheimer's disease : JAD · 2023Article
- High-Dimensional Spatial Quantile Function-on-Scalar Regression.Journal of the American Statistical Association · 2022Article
- Predicting future cognitive decline with hyperbolic stochastic coding.Medical image analysis · 2021Article
- A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.Progress in neuro-psychopharmacology & biological psychiatry · 2021Article
- Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases.Frontiers in neuroscience · 2021Article
- Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients.Brain imaging and behavior · 2020Article
- Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline.NeuroImage. Clinical · 2020Article
- Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.Journal of Alzheimer's disease : JAD · 2020Article
- Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation.Medical & biological engineering & computing · 2019Article
- Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects.NeuroImage. Clinical · 2019Article
- Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.Brain and behavior · 2017Article
- Hyperbolic Harmonic Mapping for Surface Registration.IEEE transactions on pattern analysis and machine intelligence · 2017Article
- Enhancing Diffusion MRI Measures By Integrating Grey and White Matter Morphometry With Hyperbolic Wasserstein Distance.Proceedings. IEEE International Symposium on Biomedical Imaging · 2017Article
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Authors and funding
8 authors.
Funding
Abstract
Many methods have been proposed for computer-assisted 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 tensor-based 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 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called 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 L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based 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 image-based classification.
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