ArticleScientific reports2022
Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 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
- Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023.Frontiers in neuroscience · 2024Pooled it
- Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.npj aging · 2026Article
- A deep learning approach to mild cognitive impairment detection from electroencephalogram signals.Dementia & neuropsychologia · 2026Article
- A Convolutional Mixer-Based Deep Learning Network for Alzheimer's Disease Classification from Structural Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Clinical variables contributing to the identification of biologically defined subgroups within cognitively unimpaired and mild cognitive impairment individuals.European journal of neurology · 2024Article
- Atrophy of the cholinergic regions advances from early to late mild cognitive impairment.Neuroradiology · 2024Article
- Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning.Frontiers in neuroinformatics · 2024Article
- Classification of Mild Cognitive Impairment and Alzheimer's Disease Using Manual Motor Measures.Neuro-degenerative diseases · 2024Article
- Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning.Alzheimer's research & therapy · 2023Article
- Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach.Biomedicines · 2022Article
- Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.Sensors (Basel, Switzerland) · 2022Article
- Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning.Computational intelligence and neuroscience · 2022Article
- Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of 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.