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A multiple hold-out framework for Sparse Partial Least Squares
Monteiro JM, Rao A, Shawe-Taylor J, Mourão-Miranda J, Alzheimer's Disease Initiative.. Journal of neuroscience methods, 2016-06-26
Topic: Risk, prevention and care in population studies
Background Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identification of these relationships has the potential to improve the current understanding of disease mechanisms, refine clinical assessment tools, and stratify patients. SPLS finds multivariate associative effects in the data by computing pairs of sparse weight vectors, where each pair is used to remove its corresponding associative effect from the data by matrix deflation, before computing additional pairs. New method We propose a novel SPLS framework which selects the adequate number of voxels and clinical variables to describe each associative effect, and tests their reliability by fitting the model to different splits of the data. As a proof of concept, the approach was applied to find associations between grey matter probability maps and individual items of the Mini-Mental State Examination (MMSE) in a clinical sample with various degrees of dementia. Results The framework found two statistically significant associative effects between subsets of brain voxels and subsets of the questions/tasks. Comparison with existing methods SPLS was compared with its non-sparse version (PLS). The use of projection deflation versus a classical PLS deflation was also tested in both PLS and SPLS. Conclusions SPLS outperformed PLS, finding statistically significant effects and providing higher correlation values in hold-out data. Moreover, projection deflation provided better results.
Key findings
- Results The framework found two statistically significant associative effects between subsets of brain voxels and subsets of the questions/tasks.
- Comparison with existing methods SPLS was compared with its non-sparse version (PLS).
- The use of projection deflation versus a classical PLS deflation was also tested in both PLS and SPLS.
- Conclusions SPLS outperformed PLS, finding statistically significant effects and providing higher correlation values in hold-out data.
- Moreover, projection deflation provided better results.
Why it matters
This paper describes a new statistical framework for finding relationships between brain scans and clinical test items in people with various degrees of dementia, and it found two significant associations between brain voxels and questions from a cognitive test. It is a methods paper about analysis techniques, not about treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 40/100
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 (Journal of neuroscience methods) |
| Journal's citation record | 2 / 4 | Journal of Neuroscience Methods: journal h-index 227 (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 | 1 / 5 | 2016 — within 20 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 | 4 / 7 | Cited 63 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.2 citations a year over 12 year(s) since 2016 |
| Impact for its field and year | 2 / 6 | Cited 2.3× 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 26 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Janaı́na Mourão-Miranda: h-index 43 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 27353722) |
| Free to read | 2 / 2 | Free to read |
| Total | 40 / 100 | — |
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Read the original
- DOI: 10.1016/j.jneumeth.2016.06.011
- PubMed: 27353722
- Original source →
- Free full text: Journal of Neuroscience Methods →
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
Monteiro, J. M., Rao, A., Shawe-Taylor, J., Mourão-Miranda, J., & Alzheimer's Disease Initiative. (2016). A multiple hold-out framework for Sparse Partial Least Squares. Journal of neuroscience methods. https://doi.org/10.1016/j.jneumeth.2016.06.011
Vancouver
Monteiro JM, Rao A, Shawe-Taylor J, Mourão-Miranda J, Alzheimer's Disease Initiative. A multiple hold-out framework for Sparse Partial Least Squares. Journal of neuroscience methods. 2016. doi:10.1016/j.jneumeth.2016.06.011. PMID: 27353722.
BibTeX
@article{monteiro2016-27353722,
author = {Monteiro, J. M. and Rao, A. and Shawe-Taylor, J. and Mourão-Miranda, J. and {Alzheimer's Disease Initiative}},
title = {{A multiple hold-out framework for Sparse Partial Least Squares}},
journal = {Journal of neuroscience methods},
year = {2016},
doi = {10.1016/j.jneumeth.2016.06.011},
pmid = {27353722},
url = {https://doi.org/10.1016/j.jneumeth.2016.06.011}
}
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