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Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E
Vecchio F, Miraglia F, Iberite F, Lacidogna G, Guglielmi V, Marra C, Pasqualetti P, Tiziano FD et al.. Annals of neurology, 2018-08-25
Topic: Amyloid, tau and blood-test diagnosis
Objective Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic-MCI (aMCI) subjects progress to dementia (typically to Alzheimer-Dementia = AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo-E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year. Methods 145 aMCI subjects were divided into two sub-groups and, according to the clinical follow-up, were classified as Converted to AD (C-MCI, 71) or Stable (S-MCI, 74). Results Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first-order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo-E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50% sensitivity, 69% specificity and 59.6% accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%). Interpretation Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. Ann Neurol 2018 Ann Neurol 2018;84:302-314.
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APA 7th
Vecchio, F., Miraglia, F., Iberite, F., Lacidogna, G., Guglielmi, V., Marra, C., et al. (2018). Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E. Annals of neurology. https://doi.org/10.1002/ana.25289
Vancouver
Vecchio F, Miraglia F, Iberite F, Lacidogna G, Guglielmi V, Marra C, et al. Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E. Annals of neurology. 2018. doi:10.1002/ana.25289. PMID: 30014515.
BibTeX
@article{vecchio2018-30014515,
author = {Vecchio, F. and Miraglia, F. and Iberite, F. and Lacidogna, G. and Guglielmi, V. and Marra, C. and Pasqualetti, P. and Tiziano, F. D. and others},
title = {{Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E}},
journal = {Annals of neurology},
year = {2018},
doi = {10.1002/ana.25289},
pmid = {30014515},
url = {https://doi.org/10.1002/ana.25289}
}
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Key findings
- Results Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD.
- Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97).
- Interpretation Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression.
Why it matters
In this study, combining EEG network measures with Apo-E genotyping discriminated amnestic MCI subjects who converted to Alzheimer dementia from those who stayed stable with 91.7% accuracy. This suggests a low-cost combination of tests could help estimate an individual's risk of progressing from MCI to dementia, though the findings need confirmation in larger groups.
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Study details
- Participants97
- Effect sizeAUC=0.64; 96.7%; 91.7%
- Evidence strengthLow
How this study scored 54/100
Published in a leading specialty journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Annals of neurology) |
| Journal's citation record | 3 / 4 | Annals of Neurology: journal h-index 437 (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 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 97 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 89 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.9 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 6.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 8 other studies in this library |
| Still being cited | 2 / 4 | Cited 26 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Paolo Maria Rossini: h-index 121 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Università Cattolica del Sacro Cuore: institution h-index 606 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30014515) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 54 / 100 | — |
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