ArticleBrain and behavior2017
Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.
Article in Brain and behavior, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry.Journal of Alzheimer's disease : JAD · 2023Article
- Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest.Frontiers in psychiatry · 2022Article
- Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.Emerging topics in life sciences · 2021Review
- Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology.Frontiers in neuroscience · 2021Article
- Diverse Applications of Artificial Intelligence in Neuroradiology.Neuroimaging clinics of North America · 2020Review
- Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2019Article
- A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative.Human brain mapping · 2019Article
- Understanding and predicting the longitudinal course of dementia.Current opinion in psychiatry · 2019Review
- Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis.Frontiers in aging neuroscience · 2018Article
- Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.Brain and behavior · 2017Article
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Authors and funding
7 authors.
Funding
Abstract
introductionPrediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.
methodsPrevious work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.
resultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.
conclusionsBy combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.