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Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis
Zhou T, Thung KH, Zhu X, Shen D.. Human brain mapping, 2018-11-01
Topic: Risk, prevention and care in population studies
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimaging data such as MRI and PET provide valuable insights into brain abnormalities, while genetic data such as single nucleotide polymorphism (SNP) provide information about a patient's AD risk factors. When these data are used together, the accuracy of AD diagnosis may be improved.
Key findings
- Multimodality neuroimaging data such as MRI and PET provide valuable insights into brain abnormalities, while genetic data such as single nucleotide polymorphism (SNP) provide information about a patient's AD risk factors.
- When these data are used together, the accuracy of AD diagnosis may be improved.
- However, these data are heterogeneous (e.g., with different data distributions), and have different number of samples (e.g., with far less number of PET samples than the number of MRI or SNPs).
- Thus, learning an effective model using these data is challenging.
Why it matters
This study developed a deep neural network method to combine brain imaging and genetic data for identifying Alzheimer's disease and mild cognitive impairment. It is a diagnostic tool development study and does not test any treatment, cure, or prevention.
Study details
- Evidence strengthLow
How this study scored 50/100
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 (Human brain mapping) |
| Journal's citation record | 3 / 4 | Human Brain Mapping: journal h-index 271 (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 | 2 / 5 | 2019 — within 10 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 | 5 / 7 | Cited 269 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 29.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 4 / 6 | Cited 16.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 3 / 4 | Cited 91 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 | University of North Carolina at Chapel Hill: institution h-index 1,674 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30381863) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 50 / 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
Zhou, T., Thung, K. H., Zhu, X., & Shen, D. (2019). Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Human brain mapping. https://doi.org/10.1002/hbm.24428
Vancouver
Zhou T, Thung KH, Zhu X, Shen D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Human brain mapping. 2019. doi:10.1002/hbm.24428. PMID: 30381863.
BibTeX
@article{zhou2019-30381863,
author = {Zhou, T. and Thung, K. H. and Zhu, X. and Shen, D.},
title = {{Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis}},
journal = {Human brain mapping},
year = {2019},
doi = {10.1002/hbm.24428},
pmid = {30381863},
url = {https://doi.org/10.1002/hbm.24428}
}
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