ArticleJournal of Alzheimer's disease : JAD2022
Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease.
Article in Journal of Alzheimer's disease : JAD, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Machine learning for Alzheimer's disease progression under extreme class imbalance.Frontiers in neuroscience · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.BMC medical research methodology · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study.Scientific reports · 2025Article
- Objectively Measured Physical Activity Using Wrist-Worn Accelerometers as a Predictor of Incident Alzheimer's Disease in the UK Biobank.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study.Frontiers in neuroinformatics · 2025Article
- Statistical Method for Identification of Alzheimer Disease With Multimodal Predictive Markers Mild Cognitive Impairment.Basic and clinical neuroscience · 2025Article
- Article
- Article
- Review
- A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease.Biomolecules · 2023Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.Neurobiology of aging · 2023Article
- Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?Frontiers in aging neuroscience · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThe increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT).
objectiveThe goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT.
methodsWe used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future.
resultsOur results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups.
conclusionMRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.
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.