On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations Article Swipe
Markus Helmer
,
Shaun Warrington
,
Ali‐Reza Mohammadi‐Nejad
,
Jie Lisa Ji
,
Amber Howell
,
Benjamin Rosand
,
Alan Anticevic
,
Stamatios N. Sotiropoulos
,
John D. Murray
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.08.25.265546
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.08.25.265546
Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS solutions is stability of feature patterns driving an association. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues in a systematic manner, we developed a generative modeling framework to simulate synthetic datasets, parameterized by dimensionality, variance structure, and association strength. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the latent pattern underlying the discovered association. We confirmed these trends across two neuroimaging modalities, functional and diffusion MRI, and in independent datasets, Human Connectome Project (n ≈ 1000) and UK Biobank (n ≈ 20000) and found that only the latter comprised sufficient samples for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses for future studies.
Related Topics
Concepts
Canonical correlation
Interpretability
Partial least squares regression
Stability (learning theory)
Multivariate statistics
Generalizability theory
Correlation
Computer science
Partial correlation
Pattern recognition (psychology)
Artificial intelligence
Human Connectome Project
Data mining
Statistics
Machine learning
Mathematics
Psychology
Neuroscience
Functional connectivity
Geometry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.08.25.265546
- https://www.biorxiv.org/content/biorxiv/early/2021/07/15/2020.08.25.265546.full.pdf
- OA Status
- green
- Cited By
- 75
- References
- 113
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3080315277
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3080315277Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.08.25.265546Digital Object Identifier
- Title
-
On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-25Full publication date if available
- Authors
-
Markus Helmer, Shaun Warrington, Ali‐Reza Mohammadi‐Nejad, Jie Lisa Ji, Amber Howell, Benjamin Rosand, Alan Anticevic, Stamatios N. Sotiropoulos, John D. MurrayList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.08.25.265546Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/07/15/2020.08.25.265546.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/07/15/2020.08.25.265546.full.pdfDirect OA link when available
- Concepts
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Canonical correlation, Interpretability, Partial least squares regression, Stability (learning theory), Multivariate statistics, Generalizability theory, Correlation, Computer science, Partial correlation, Pattern recognition (psychology), Artificial intelligence, Human Connectome Project, Data mining, Statistics, Machine learning, Mathematics, Psychology, Neuroscience, Functional connectivity, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
75Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 11, 2023: 18, 2022: 22, 2021: 17, 2020: 7Per-year citation counts (last 5 years)
- References (count)
-
113Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.association. | 37, 112 |
| abstract_inverted_index.associations | 93 |
| abstract_inverted_index.multivariate | 8, 174 |
| abstract_inverted_index.neuroimaging | 119 |
| abstract_inverted_index.parameterized | 70 |
| abstract_inverted_index.questionable, | 46 |
| abstract_inverted_index.dimensionality, | 72 |
| abstract_inverted_index.imaging-derived | 154 |
| abstract_inverted_index.generalizability | 26 |
| abstract_inverted_index.high-dimensional | 43 |
| abstract_inverted_index.interpretability | 24 |
| abstract_inverted_index.characterizations. | 51 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5052140351, https://openalex.org/A5024452548 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I142263535, https://openalex.org/I2802742124, https://openalex.org/I32971472, https://openalex.org/I34931013, https://openalex.org/I40120149, https://openalex.org/I4210101881, https://openalex.org/I4210137227, https://openalex.org/I4210146512 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.9809416 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |