ArticleTheScientificWorldJournal2014
Significance of normalization on anatomical MRI measures in predicting Alzheimer's disease.
Article in TheScientificWorldJournal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- A practical guideline for intracranial volume estimation in patients with Alzheimer's disease.BMC bioinformatics · 2015Guideline
- Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease.Research square · 2024Article
- Development of Human Lateral Prefrontal Sulcal Morphology and Its Relation to Reasoning Performance.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Impact of sex and APOE-Frontiers in neurology · 2023Article
- Article
- Improving Accuracy of Brainstem MRI Volumetry: Effects of Age and Sex, and Normalization Strategies.Frontiers in neuroscience · 2020Article
- A physical model for dementia.Physica A · 2017Article
- Is there a relation between novelty seeking, striatal dopamine release and frontal cortical thickness?PloS one · 2017Article
- Predictive Model of Spread of Progressive Supranuclear Palsy Using Directional Network Diffusion.Frontiers in neurology · 2017Article
- Estimating Intracranial Volume in Brain Research: An Evaluation of Methods.Neuroinformatics · 2015Article
- Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment.Computational intelligence and neuroscience · 2015Article
- Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer's disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or mean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD.
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Registered trials
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.