Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/jon.13063

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Figures

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Research works printed but not linked: 59

  1. 10.1016/j.cct.2021.106549 · DOI
  2. 10.1016/S2666-7568(21)00140-9 · DOI
  3. 10.1177/1471301212442453 · DOI
  4. 10.1016/B978-0-12-821334-6.00001-6 · DOI
  5. Arevalo‐Rodriguez I, 2015, Mini‐Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI), Cochrane Database Syst Rev, 2015, CD010783
  6. 10.1111/ijcp.13265 · DOI
  7. 10.3390/brainsci11070881 · DOI
  8. 10.1016/B978-0-12-821334-6.00004-1 · DOI
  9. 10.1016/j.bionps.2019.100005 · DOI
  10. 10.1007/s00221-021-06182-w · DOI
  11. Matthews PM, 2004, Functional magnetic resonance imaging, J Neurol Neurosurg Psychiatry, 75, 6
  12. 10.1007/978-3-030-00931-1_34 · DOI
  13. 10.3389/fpubh.2020.584430 · DOI
  14. 10.1155/2009/610392 · DOI
  15. 10.3233/JAD-170893 · DOI · Paper page
  16. 10.1007/s12017-009-8109-7 · DOI
  17. 10.1016/j.neurobiolaging.2011.09.009 · DOI · Paper page
  18. Ashburner J, 2014, SPM12 manual
  19. 10.1016/j.neuroimage.2011.09.015 · DOI · Paper page
  20. 10.1093/scan/nsm006 · DOI
  21. 10.1016/j.conb.2003.09.012 · DOI
  22. 10.1002/ima.20166 · DOI
  23. 10.1016/j.neurobiolaging.2010.04.013 · DOI
  24. 10.1212/01.WNL.0000079052.01016.78 · DOI
  25. 10.1016/j.nicl.2017.01.018 · DOI · Paper page
  26. 10.1016/j.jbi.2009.10.004 · DOI
  27. 10.1016/B978-0-12-821334-6.00002-8 · DOI
  28. 10.1016/j.media.2021.102057 · DOI
  29. 10.1016/j.neuroimage.2020.117340 · DOI
  30. 10.1117/1.JMI.7.5.056001 · DOI
  31. Kazemi Y, 2018, A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data (abstract), IEEE CIBCB, 2018, 1
  32. 10.1111/exsy.12644 · DOI
  33. 10.1109/ACCESS.2020.2989396 · DOI
  34. 10.1016/j.cmpb.2019.105242 · DOI
  35. Abrol A, 2019, Multimodal data fusion of deep learning and dynamic functional connectivity features to predict Alzheimer's disease progression (abstract), IEEE EMBC, 2019, 4409
  36. Wang Y, 2018, A Novel Multimodal MRI analysis for Alzheimer's disease based on convolutional neural network, IEEE EMBC, 2018, 754
  37. Wang Y, 2021, Assisted diagnosis of Alzheimer's disease based on deep learning and multimodal feature fusion, Complexity, 2021, e6626728, 10.1155/2021/6626728 · DOI
  38. 10.1016/j.neuroimage.2021.118048 · DOI
  39. 10.1016/j.neucom.2020.01.053 · DOI
  40. 10.1016/j.nicl.2020.102186 · DOI
  41. 10.1016/j.media.2020.101709 · DOI
  42. 10.1016/j.neuroimage.2019.116189 · DOI
  43. 10.3389/fnagi.2018.00417 · DOI
  44. 10.3389/fnins.2014.00167 · DOI
  45. 10.1007/s12021-019-09419-w · DOI
  46. 10.1109/TMI.2019.2928790 · DOI
  47. 10.1016/j.compbiomed.2020.104096 · DOI
  48. 10.3389/fnins.2018.00413 · DOI
  49. 10.3389/fnagi.2019.00008 · DOI
  50. 10.1109/ACCESS.2019.2949577 · DOI
  51. 10.1109/TCBB.2017.2776910 · DOI
  52. 10.1038/s41746-021-00521-5 · DOI · Paper page
  53. 10.1016/j.media.2021.102082 · DOI
  54. 10.1016/j.media.2020.101947 · DOI
  55. 10.1016/j.neuroimage.2016.01.005 · DOI
  56. 10.1016/j.neunet.2020.12.005 · DOI
  57. 10.1109/TBME.2019.2957921 · DOI
  58. 10.1016/j.neuroimage.2011.09.069 · DOI
  59. 10.1007/s10916-019-1475-2 · DOI

Cited by

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)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

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).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.