Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease

the Alzheimer’s Disease Neuroimaging Initiative; Habeck, Christian; Stern, Yaakov

doi:10.1007/s12013-010-9093-0

Cited by 127Functional Brain Connectivity StudiesDementia and Cognitive Impairment ResearchStatistical Methods and Applications

Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease

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the Alzheimer’s Disease Neuroimaging Initiative, Habeck, Christian, Stern, Yaakov·2010·Cell Biochemistry and Biophysics

DOI 10.1007/s12013-010-9093-0PMC PMC3001346

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Abstract

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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One widely used hypothesis-driven analysis assesses statistical differences of tracts of interest between groups using statistical group-wise comparison methods such as Student's t-test, ANOVA, or more advanced methods.
“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
The Toy Example section (page unavailable) describes a standard group comparison using a univariate T-test, supporting that part of the citation. It does not describe extracting mean microstructural measures from tracts of interest or comparing those summaries with ANOVA; its example compares voxels and PCA subject scores instead. (LLM, conf=0.84)
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Authors: Initiative, T. A. D. N., Habeck, C., & Stern, Y.
Affiliations: Columbia University (US)
Corresponding author: Habeck, Christian
ORCID: Habeck, Christian
Stern, Yaakov
Venue: Cell Biochemistry and Biophysics
Publisher: Springer Science and Business Media LLC
Published: 2010-07-24
Volume: 58
Issue: 2
Pages: 53-67
ISSN: 1085-9195 (ISSN-L)
1559-0283
DOI: 10.1007/s12013-010-9093-0
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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Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease

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