ArticleJournal of Alzheimer's disease : JAD2018
The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1.
Article in Journal of Alzheimer's disease : JAD, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.Neuroinformatics · 2021Pooled it
- Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Temporal Harmonization: Improved Detection of Mild Cognitive Impairment from Temporal Language Markers using Subject-invariant Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Detection of Mild Cognitive Impairment from Language Markers with Crossmodal Augmentation.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Review
- Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.Frontiers in aging neuroscience · 2022Article
- Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer's Disease.Entropy (Basel, Switzerland) · 2019Article
- Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.
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Registered trials
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