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Cited by 161

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Warren, Samuel L., Moustafa, Ahmed A.·2023·Journal of Neuroimaging
DOI 10.1111/jon.13063PMC PMC10092597
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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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FIGURE 1 The study selection process for our systematic review using the PRISMA flowchart. 68 n = participant count. Use of this flowchart is recommended by Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) to meet their guidelines for conducting systematic reviews.
FIGURE 1 The study selection process for our systematic review using the PRISMA flowchart. 68 n = participant count. Use of this flowchart is recommended by Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) to meet their guidelines for conducting systematic reviews.CC BY-NC · PubMed Central

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Metadata

Authors
Warren, S. L., & Moustafa, A. A.
Venue
Journal of Neuroimaging
Publisher
Wiley
Published
2022-10-18
Volume
33
Issue
1
Pages
5-18
ISSN
  • 1051-2284
  • 1552-6569
DOI
10.1111/jon.13063
PMC
PMC10092597
Cited by
161
References
7
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)

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CoChat. (2026). Citation check: Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review. https://app.cochat.ai/papers/10.1111/jon.13063
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Warren, S. L., & Moustafa, A. A. (2023). Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review. Journal of Neuroimaging. https://doi.org/10.1111/jon.13063

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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