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Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study
Zhengping Pu, Hongna Huang, Man Li, Hongyan Li, Xiaoyan Shen, Lizhao Du, Qingfeng Wu, Xiaomei Fang et al.. NeuroImage, 2025-03-07
Topic: Risk, prevention and care in population studies
Statistical analysis revealed a trend toward more impaired prefrontal FC with declining cognitive function. Prediction models were built by combining features of prefrontal FC and cognitive scale performance and applying machine learning models, The models showed generally satisfactory abilities to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 92.0 % for MCI vs. NC, 80.0 % for MCI vs. SCD, and 76.1 % for SCD vs. NC were achieved, and the highest AUC values were 97.0 % (95 % CI: 94.6 %-99.3 %) for MCI vs. NC, 87.0 % (95 % CI: 81.5 %-92.5 %) for MCI vs. SCD, and 79.2 % (95 % CI: 71.0 %-87.3 %) for SCD vs. NC. The developed screening method based on fNIRS and machine learning has the potential to predict early-stage cognitive impairment based on prefrontal FC data collected during cognitive scale-induced activation.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Pu, Z., Huang, H., Li, M., Li, H., Shen, X., Du, L., et al. (2025). Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study. NeuroImage. https://doi.org/10.1016/j.neuroimage.2025.121130
Vancouver
Pu Z, Huang H, Li M, Li H, Shen X, Du L, et al. Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study. NeuroImage. 2025. doi:10.1016/j.neuroimage.2025.121130. PMID: 40058532.
BibTeX
@article{pu2025-40058532,
author = {Pu, Zhengping and Huang, Hongna and Li, Man and Li, Hongyan and Shen, Xiaoyan and Du, Lizhao and Wu, Qingfeng and Fang, Xiaomei and others},
title = {{Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study}},
journal = {NeuroImage},
year = {2025},
doi = {10.1016/j.neuroimage.2025.121130},
pmid = {40058532},
url = {https://doi.org/10.1016/j.neuroimage.2025.121130}
}
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
- Statistical analysis revealed a trend toward more impaired prefrontal FC with declining cognitive function.
- Prediction models were built by combining features of prefrontal FC and cognitive scale performance and applying machine learning models, The models showed generally satisfactory abilities to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine.
- NC were achieved, and the highest AUC values were 97.0 % (95 % CI: 94.6 %-99.3 %) for MCI vs.
- NC, 87.0 % (95 % CI: 81.5 %-92.5 %) for MCI vs.
- SCD, and 79.2 % (95 % CI: 71.0 %-87.3 %) for SCD vs.
Why it matters
Using functional near-infrared spectroscopy and machine learning, this study built models that differentiated mild cognitive impairment, subjective cognitive decline and normal cognition with accuracies from 76.1% to 92.0%. This is a potential screening method for early-stage cognitive impairment, not a treatment, cure or prevention.
Study details
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, 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 (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (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 | 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 16 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 10.2× 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 | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Donghong Cui: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Shanghai Jiao Tong University: institution h-index 975 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40058532) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2025.121130
- PubMed: 40058532
- Original source →
- Free full text: NeuroImage →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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