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Detecting Mild Cognitive Impairment to Alzheimer’s Disease Progression by fMRI Using Convolutional Neural Network and Long-short Term Memory
Sima Ghafoori, Ahmad Shalbaf. Basic and Clinical Neuroscience Journal, 2025-11-01
Topic: Amyloid, tau and blood-test diagnosis
The results showed that the best validation scores were achieved by the CNN-LSTM model after integrating clinical attributes, with an accuracy of 92.47%. The proposed algorithm demonstrated high performance in predicting MCI-to-AD progression, indicating the potential of DL approaches for processing fMRI data and the efficiency of data type integration.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Ghafoori, S., & Shalbaf, A. (2025). Detecting Mild Cognitive Impairment to Alzheimer’s Disease Progression by fMRI Using Convolutional Neural Network and Long-short Term Memory. Basic and Clinical Neuroscience Journal. https://doi.org/10.32598/bcn.2025.2034.8
Vancouver
Ghafoori S, Shalbaf A. Detecting Mild Cognitive Impairment to Alzheimer’s Disease Progression by fMRI Using Convolutional Neural Network and Long-short Term Memory. Basic and Clinical Neuroscience Journal. 2025. doi:10.32598/bcn.2025.2034.8. PMID: 42238745.
BibTeX
@article{ghafoori2025-42238745,
author = {Ghafoori, Sima and Shalbaf, Ahmad},
title = {{Detecting Mild Cognitive Impairment to Alzheimer’s Disease Progression by fMRI Using Convolutional Neural Network and Long-short Term Memory}},
journal = {Basic and Clinical Neuroscience Journal},
year = {2025},
doi = {10.32598/bcn.2025.2034.8},
pmid = {42238745},
url = {https://doi.org/10.32598/bcn.2025.2034.8}
}
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.
Key findings
- The results showed that the best validation scores were achieved by the CNN-LSTM model after integrating clinical attributes, with an accuracy of 92.47%.
- The proposed algorithm demonstrated high performance in predicting MCI-to-AD progression, indicating the potential of DL approaches for processing fMRI data and the efficiency of data type integration.
Why it matters
A deep learning model using fMRI data and clinical attributes predicted progression from mild cognitive impairment to Alzheimer's disease with 92.47% accuracy. This is a prediction tool, not a treatment, cure or prevention for Alzheimer's disease.
Study details
- Effect size92.47%
- Evidence strengthLow
How this study scored 34/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Basic and Clinical Neuroscience Journal) |
| Journal's citation record | 0 / 4 | Basic and Clinical Neuroscience Journal: journal h-index 30 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ahmad Shalbaf: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Shahid Beheshti University of Medical Sciences: institution h-index 381 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 42238745) |
| Free to read | 2 / 2 | Free to read |
| Total | 34 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.32598/bcn.2025.2034.8
- PubMed: 42238745
- Original source →
- Free full text: Basic and Clinical Neuroscience Journal →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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