The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index.
Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease
Open accessAbstract
As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.
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“Usually, given a quantitative measure of interest, a selected summary statistic (e.g. mean of the chosen microstructural measure) is extracted from the tract(s)-of-interest and is used to compute the level of group differences (e.g., the p-value) using statistical group-wise comparison methods such as Student’s t-test, ANOVA, or other more advanced statistical analysis methods (Habeck and Stern, 2”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Habeck and Stern, 2010
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The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.05.1769 · Cited by 1,033
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.09.172 · Cited by 497
The relation between statistical power and inference in fMRI
Cremers, H. R., Wager, T. D., & Yarkoni, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184923 · Cited by 356
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348
Brakedal, B., Dölle, C., Riemer, F., Ma, Y., Nido, G. S., Skeie, G. O., Craven, A. R., Schwarzlmüller, T., Brekke, N., Diab, J., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.02.001 · Cited by 323
Metadata
- Authors
- Initiative, T. A. D. N., Habeck, C., & Stern, Y.
- Affiliations
- Columbia University (US)
- Corresponding author
- Habeck, Christian
- Venue
- Cell Biochemistry and Biophysics
- Published
- 2010-07-24
- Volume
- 58
- Issue
- 2
- Pages
- 53-67
- ISSN
- 1085-9195 (ISSN-L)
- 1559-0283
- PMC
- PMC3001346
- OpenAlex
- W2081432693
- Cited by
- 127
- References
- 49
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 6 · 2023: 6 · 2022: 14 · 2021: 7 · 2020: 10 · 2019: 5 · 2018: 11 · 2017: 7 · 2016: 7 · 2015: 8 · 2014: 8 · 2013: 6 · 2012: 15
- FWCI
- 0.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Cell Biochemistry and Biophysics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Columbia Academic Commons (Columbia University)
- Funders
- National Institutes of Health (5u01ag024904-10)
- National Institute on Aging (U01-AG-024904, P30AG010129, R01AG026114, R01AG026158, K01AG030514, U19AG010483)
- National Institute of Biomedical Imaging and Bioengineering (R01EB006204)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- multivariate analysis, neuroimaging, Alzheimer's disease, functional connectivity, covariance analysis, artificial neural networks, statistical power, multiple testing correction, reproducibility
- MeSH terms
- Alzheimer Disease, Brain, Humans, Image Processing, Computer-Assisted, Multivariate Analysis, Principal Component Analysis, Molecular Imaging
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