ReviewNeuroImage2017
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.
Review in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 236 papers, 9 of them syntheses 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
236 citing papers in PubMed, 9 syntheses or guidelines pooled it, 614 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
- Multimodal Deep Learning Approaches for Early Detection of Alzheimer's Disease: A Comprehensive Systematic Review of Image Processing Techniques.Current Alzheimer research · 2025Pooled it
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- Diagnostic value of arterial spin labeling for Alzheimer's disease: A systematic review and meta-analysis.PloS one · 2024Pooled it
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.NeuroImage · 2023Pooled it
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.Neuroinformatics · 2021Pooled it
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.Annals of nuclear medicine · 2026Article
- Deep survival modeling to predict future cognitive impairment in unimpaired adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Reducing Educational Bias in Cognitive Assessment via Dynamic Support Vector Machine Weighting: Validation Study on an Education-Stratified Dataset.JMIR rehabilitation and assistive technologies · 2026Article
- Predicting the progression of MCI and Alzheimer's disease on structural brain integrity and other features with machine learning.GeroScience · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Spatiotemporal profiling of functional network overlapping modules in Alzheimer's disease.Network neuroscience (Cambridge, Mass.) · 2026Article
- Alzheimer's disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning.Frontiers in neuroscience · 2026Article
- Multimodal MRI to elucidate cross-organ coupling mechanisms of the heart-brain axis.Frontiers in cardiovascular medicine · 2026Review
- Cortical layer multi-parameter analysis of neurovascular impairments in AD/ADRD rodent model with in vivo optical imaging.Translational neurodegeneration · 2025Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.Scientific reports · 2025Article
176 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
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.