This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

38/100

✓ Peer-reviewed alzhub usefulness score 38 out of 100. Mild cognitive impairment

Topic: Brain proteins, microglia and lab models

Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149). The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Yuan, Z., Huang, Z., Li, C., Li, S., Ren, Q., Xia, X., et al. (2025). Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2025.1527323

Vancouver

Yuan Z, Huang Z, Li C, Li S, Ren Q, Xia X, et al. Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease. Frontiers in Aging Neuroscience. 2025. doi:10.3389/fnagi.2025.1527323. PMID: 40013095.

BibTeX

@article{yuan2025-40013095,
  author  = {Yuan, Ziyi and Huang, Zhaodi and Li, Chaojun and Li, Shengrong and Ren, Qingguo and Xia, Xiaona and Jiang, Qingjun and Zhang, Daoqiang and others},
  title   = {{Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease}},
  journal = {Frontiers in Aging Neuroscience},
  year    = {2025},
  doi     = {10.3389/fnagi.2025.1527323},
  pmid    = {40013095},
  url     = {https://doi.org/10.3389/fnagi.2025.1527323}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.

Key findings

Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%.
The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).
The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections.
Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.

Why it matters

A multimodal fusion model diagnosed mild cognitive impairment in patients with unilateral middle cerebral artery steno-occlusive disease with 90% accuracy and an AUC of 0.9149. The authors say efficient automated diagnosis could facilitate early detection and timely intervention to delay or prevent disease progression, but this is a diagnostic tool.

Study details

Effect size90.00%; AUC = 0.9149
Evidence strengthLow

How this study scored 38/100

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. We have no data on senior author's track record, so that was left out: it earned 36 of the 96 points we could judge it on, which is 38 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in Aging Neuroscience)
Journal's citation record	2 / 4	Frontiers in Aging Neuroscience: journal h-index 174 (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	3 / 3	Effect reported with a statistic (interval, p-value, ratio or similar)
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 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 1.1× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author Xiangshui Meng holds only 7 work(s), too few to judge a track record — left out of this score
Institution's track record	0 / 3	Shandong University: institution h-index 615 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 4 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40013095)
Free to read	2 / 2	Free to read
Points	36 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 36 of the 96 points we could judge it on is 38 out of 100.
Total	38 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

Related studies in Mild cognitive impairment

Also tagged “vessel”

900 studies in the library carry the topic tag “vessel”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 900 tagged studies.

Similar studies

More on Mild cognitive impairment

See all studies in Mild cognitive impairment →