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Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia
Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, Zuo C, Yu J et al.. European journal of nuclear medicine and molecular imaging, 2020-04-22
Topic: Brain proteins, microglia and lab models
Purpose Positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual's risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual's risk of conversion from MCI to AD.
Key findings
- Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual's risk of conversion from present MCI to AD.
- We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual's risk of conversion from MCI to AD.
- Methods FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database.
- Each individual's metabolic brain network was ascertained using the KLSE method.
Why it matters
This study developed a method using FDG-PET brain metabolic networks to predict an individual's risk of conversion from mild cognitive impairment to Alzheimer's dementia. It does not show that any treatment prevents conversion, and the work is about prediction rather than curing or preventing dementia.
Study details
- Participants332
- Evidence strengthLow
How this study scored 50/100
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 senior author's track record, so that was left out: it earned 48 of the 96 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (European journal of nuclear medicine and molecular imaging) |
| Journal's citation record | 2 / 4 | European Journal of Nuclear Medicine and Molecular Imaging: journal h-index 224 (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 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 332 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 | 4 / 7 | Cited 97 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.1 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 8.3× 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 | 2 / 4 | Cited 45 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 1 / 3 | Technical University of Munich: institution h-index 1,092 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 7 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32318784) |
| Free to read | 2 / 2 | Free to read |
| Points | 48 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 48 of the 96 points we could judge it on is 50 out of 100. |
| Total | 50 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1007/s00259-020-04814-x
- PubMed: 32318784
- Original source →
- Free full text: European Journal of Nuclear Medicine and Molecular Imaging →
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. 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.
APA 7th
Wang, M., Jiang, J., Yan, Z., Alberts, I., Ge, J., Zhang, H., et al. (2020). Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. European journal of nuclear medicine and molecular imaging. https://doi.org/10.1007/s00259-020-04814-x
Vancouver
Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, et al. Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. European journal of nuclear medicine and molecular imaging. 2020. doi:10.1007/s00259-020-04814-x. PMID: 32318784.
BibTeX
@article{wang2020-32318784,
author = {Wang, M. and Jiang, J. and Yan, Z. and Alberts, I. and Ge, J. and Zhang, H. and Zuo, C. and Yu, J. and others},
title = {{Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia}},
journal = {European journal of nuclear medicine and molecular imaging},
year = {2020},
doi = {10.1007/s00259-020-04814-x},
pmid = {32318784},
url = {https://doi.org/10.1007/s00259-020-04814-x}
}
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