SynthesisJournal of neuroimaging : official journal of the American Society of Neuroimaging2023
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.
Synthesis in Journal of neuroimaging : official journal of the American Society of Neuroimaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 4 of them syntheses that pooled it.
What it found
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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
58 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- What patient reported outcome measures are used in clinical trials in hip fracture? A systematic mapping review.BMC musculoskeletal disorders · 2026Pooled it
- Smoking cessation in pregnancy: a systematic review and meta-analysis of vulnerability approaches and expectant father involvement.Public health reviews · 2026Pooled it
- The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2023Pooled it
- Integration of functional magnetic resonance imaging and artificial intelligence in Alzheimer's disease.Neural regeneration research · 2026Article
- Identifying cognitive impairment in older adults using machine learning on combined fNIRS and motion data during an upper extremity dual task function.Research square · 2026Article
- An AI-augmented review of childhood atopic dermatitis biomarkers across genetic, immune, microbial, and metabolic domains.Molecular medicine (Cambridge, Mass.) · 2026Review
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- The Default Mode Network and Behavior: a Model to Analyse Psycho-Physiological Interactions in Resting State fMRI.Brain topography · 2026Article
- Changes in Functional Brain Activity in School-Age Children 10 Years Later: A Resting State Functional Magnetic Resonance Imaging Study.Psychiatry investigation · 2026Article
- When to stop reviewing: validation of stop criteria in ASReview.BMC medical research methodology · 2026Article
- Resting-state fMRI foundation models enable robust and generalizable latent neural target discovery in cognitive aging interventions.bioRxiv : the preprint server for biology · 2026Article
- Whole-Brain Static Functional Connectivity Disruptions Based on the Default Mode Network in Patients with Mild Cognitive Impairment.Brain topography · 2026Article
- Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.Brain and behavior · 2026Article
- Mitochondrial dysfunction in Alzheimer's disease: connecting pathophysiology with neuroimaging.Frontiers in aging neuroscience · 2026Review
- Precise diagnosis of Alzheimer's disease based on sex-specific gray matter characteristics.Frontiers in neuroscience · 2026Article
- The Role of Microglial Cells and Cytokine Modulation in Alzheimer's Disease: A Neuroinflammatory Perspective.Current Alzheimer research · 2026Review
- Neurometabolic and vascular dysfunction as an early diagnostic for Alzheimer's disease and related dementias.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Detection and classification of supraspinatus pathologies on shoulder magnetic resonance images using a code-free deep learning application.Asia-Pacific journal of sports medicine, arthroscopy, rehabilitation and technology · 2025Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.
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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.