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A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease
Spasov S, Passamonti L, Duggento A, Liò P, Toschi N, Alzheimer's Disease Neuroimaging Initiative.. NeuroImage, 2019-01-14
Topic: Amyloid, tau and blood-test diagnosis
The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data.
Key findings
- The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data.
Why it matters
This paper describes a deep learning model that uses structural MRI images and warp field characteristics to predict conversion from mild cognitive impairment to Alzheimer's disease. The model uses about 550,000 parameters and is designed to limit data overfitting, but the text does not report how well it performed in practice.
Study details
- Participants785
- Effect size87.5%
- Evidence strengthLow
How this study scored 62/100
Published in a peer-reviewed journal. Scored zero for study design 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 | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 785 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 6 / 7 | Cited 428 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 47.6 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 5 / 6 | Cited 33.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 13 other studies in this library |
| Still being cited | 3 / 4 | Cited 163 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Nicola Toschi: h-index 54 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30654174) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 62 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2019.01.031
- PubMed: 30654174
- 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).
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
Spasov, S., Passamonti, L., Duggento, A., Liò, P., Toschi, N., & Alzheimer's Disease Neuroimaging Initiative. (2019). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. NeuroImage. https://doi.org/10.1016/j.neuroimage.2019.01.031
Vancouver
Spasov S, Passamonti L, Duggento A, Liò P, Toschi N, Alzheimer's Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. NeuroImage. 2019. doi:10.1016/j.neuroimage.2019.01.031. PMID: 30654174.
BibTeX
@article{spasov2019-30654174,
author = {Spasov, S. and Passamonti, L. and Duggento, A. and Liò, P. and Toschi, N. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease}},
journal = {NeuroImage},
year = {2019},
doi = {10.1016/j.neuroimage.2019.01.031},
pmid = {30654174},
url = {https://doi.org/10.1016/j.neuroimage.2019.01.031}
}
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Referenced by this study and also in the alzhub library, so you can read them here.
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