ArticleComputational intelligence and neuroscience2015
Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment.
Article in Computational intelligence and neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort.Brain and behavior · 2024Article
- Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine.BMC medical imaging · 2024Article
- A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.Brain informatics · 2023Article
- Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI.Annals of translational medicine · 2022Article
- Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data.Scientific reports · 2022Article
- Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.Frontiers in aging neuroscience · 2022Article
- Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia.Frontiers in aging neuroscience · 2022Article
- Novel characterization of the relationship between verbal list-learning outcomes and hippocampal subfields in healthy adults.Human brain mapping · 2021Article
- Endocytosis-pathway polygenic scores affects the hippocampal network connectivity and individualized identification across the high-risk of Alzheimer's disease.Brain imaging and behavior · 2021Article
- Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion.Frontiers in neuroscience · 2021Article
- Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.IEEE transactions on bio-medical engineering · 2020Article
- A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.Journal of neuroscience methods · 2020Article
- Word Meaning Contributes to Free Recall Performance in Supraspan Verbal List-Learning Tests.Frontiers in psychology · 2020Article
- Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: A Resting-State fMRI Study.Frontiers in aging neuroscience · 2019Article
- Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.Frontiers in psychiatry · 2019Article
- Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states.Scientific reports · 2018Article
- Analysis of Progression Toward Alzheimer's Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster.Frontiers in neuroscience · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.
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