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Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network
Wang M, Lian C, Yao D, Zhang D, Liu M, Shen D.. IEEE transactions on bio-medical engineering, 2019-12-06
Topic: Brain proteins, microglia and lab models
Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression.
Key findings
- Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency.
- Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression.
- In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series.
- Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model.
Why it matters
This paper proposes a computer method for analyzing functional MRI to help diagnose Alzheimer's disease at the mild cognitive impairment stage. It is a diagnostic technique and does not provide a treatment, cure, or prevention.
Study details
- Participants174
- Evidence strengthLow
How this study scored 53/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, so that was left out: it earned 51 of the 96 points we could judge it on, which is 53 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE transactions on bio-medical engineering) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 174 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 162 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 20.2 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 7.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 79 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Dinggang G. Shen: h-index 149 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Chinese Academy of Sciences: institution h-index 1,801 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31825859) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 51 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 51 of the 96 points we could judge it on is 53 out of 100. |
| Total | 53 / 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).
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
Wang, M., Lian, C., Yao, D., Zhang, D., Liu, M., & Shen, D. (2020). Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network. IEEE transactions on bio-medical engineering. https://doi.org/10.1109/tbme.2019.2957921
Vancouver
Wang M, Lian C, Yao D, Zhang D, Liu M, Shen D. Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network. IEEE transactions on bio-medical engineering. 2020. doi:10.1109/tbme.2019.2957921. PMID: 31825859.
BibTeX
@article{wang2020-31825859,
author = {Wang, M. and Lian, C. and Yao, D. and Zhang, D. and Liu, M. and Shen, D.},
title = {{Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network}},
journal = {IEEE transactions on bio-medical engineering},
year = {2020},
doi = {10.1109/tbme.2019.2957921},
pmid = {31825859},
url = {https://doi.org/10.1109/tbme.2019.2957921}
}
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