ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Early axonal degeneration linked to clinical decline in Alzheimer's disease progression revealed with diffusion MRI.The Journal of clinical investigation · 2026Trial
- Article
- Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prexdiction with Multimodal Biomedical Data.ArXiv · 2026Article
- Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- An interpretable machine learning screening model for MoCA-defined possible mild cognitive impairment in rural Xinjiang: a preliminary framework for resource-limited primary care.Frontiers in public health · 2026Article
- Axonal Degeneration Across the Alzheimer's Disease Spectrum: A Longitudinal MRI and Fluid Biomarker Study.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMachine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain structure and function. The effectiveness of advanced diffusion MRI (dMRI) methods for pre-dementia classification remains largely unexplored, particularly when combined with CSF biomarkers.
methodsWe implemented XGBoost machine learning models to evaluate the classification potential of dMRI parameters (derived using NODDI, C-NODDI, MAP, or SMI), CSF biomarkers of Alzheimer's pathology (Tau, pTau, Aβ42, Aβ40), and pairwise dMRI + CSF combinations in distinguishing cognitive normality from mild cognitive impairment.
resultsMAP-RTAP (AUC = 0.78) and pTau/Aβ42 (AUC = 0.76) were the best performing individual biomarkers. Combining C-NDI derived using C-NODDI and Aβ42/Aβ40 achieved the highest performance (AUC = 0.84) and accuracy (0.84), while other combinations optimized either sensitivity (0.93) or specificity (0.88). DISCUSSION: dMRI biomarkers demonstrate comparable performance to CSF biomarkers, with notable improvements achieved when combined. This study highlights dMRI's effectiveness for enhancing early AD detection. Highlights: Advanced multishell diffusion MRI provides equivalent performance as CSF biomarkers in classifying MCICombining diffusion MRI and CSF biomarkers improves classification performanceStatistical diffusion MRI models perform best when used individually to classify MCIThe pTau/Aβ42 ratio outperforms other individual CSF biomarkers in MCI diagnosisBiophysical diffusion MRI models achieve the best performance when combined with CSF data.
Indexed as
Identifiers
What OpenQuestion holds
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.