ArticleFrontiers in neuroinformatics2011
High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization.
Article in Frontiers in neuroinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Exploring Alzheimer Disease from a Retinal and Ocular Perspective.Biomedicines · 2026Review
- Integrative gene co-expression network analysis reveals protein-coding and LncRNA genes associated with Alzheimer's disease pathology.Scientific reports · 2025Article
- Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.GeroScience · 2024Article
- The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes.Alzheimer's research & therapy · 2024Article
- Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches.Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer's disease and frontotemporal dementia using genetic algorithms.Medical & biological engineering & computing · 2022Article
- Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Folded concave penalized learning of high-dimensional MRI data in Parkinson's disease.Journal of neuroscience methods · 2021Article
- Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players.Frontiers in neuroscience · 2021Article
- Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer's Disease Prediction.IEEE transactions on medical imaging · 2020Article
- Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction.Aging · 2020Article
- Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volumeJournal of psychiatry & neuroscience : JPN · 2020Article
- Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease.Brain : a journal of neurology · 2020Article
- Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning.Scientific reports · 2019Article
- Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain.NeuroImage · 2019Article
- The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease.Neuroinformatics · 2019Article
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Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.
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