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fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review
Maitha Alarjani, Badar Almarri. PeerJ. Computer science, 2024-10-16
Topic: Brain imaging, connectivity and assessment
Alzheimer's disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer's disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer's in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer's disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer's disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.
Key findings
- Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging.
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Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 71/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 40 of the 56 points we could judge it on, which is 71 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PeerJ. Computer science) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39650470) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 40 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 40 of the 56 points we could judge it on is 71 out of 100. |
| Total | 71 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Alarjani, M., & Almarri, B. (2024). fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review. PeerJ. Computer science. https://doi.org/10.7717/peerj-cs.2302
Vancouver
Alarjani M, Almarri B. fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review. PeerJ. Computer science. 2024. doi:10.7717/peerj-cs.2302. PMID: 39650470.
BibTeX
@article{alarjani2024-39650470,
author = {Alarjani, Maitha and Almarri, Badar},
title = {{fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review}},
journal = {PeerJ. Computer science},
year = {2024},
doi = {10.7717/peerj-cs.2302},
pmid = {39650470},
url = {https://doi.org/10.7717/peerj-cs.2302}
}
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