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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. (1995) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983304 · Cited by 22,453
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340
An Introduction to the Bootstrap.
Buckland, S. T., Efron, B., & Tibshirani, R. J. (1994) · Biometrics · doi:10.2307/2532810 · Cited by 3,236
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging
Beckmann, C. F., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.822821 · Cited by 2,707
Circular analysis in systems neuroscience: the dangers of double dipping
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674
Cited by
Sorted by citations
Items 101–127 of 127
Imaging of Disease-Related Networks in Parkinson's DiseasePreprint
Nakano, Y., Niethammer, M., & Eidelberg, D. (2025) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041841 · Cited by 4
Statistical model for IC50 determination of acetylcholinesterase enzyme for Alzheimer’s disease
Fitrianto, A., Mah, S. M., & Mah, S. H. (2022) · International Journal of Public Health Science (IJPHS) · doi:10.11591/ijphs.v11i3.21282 · Cited by 3
Ding, C. H., Yu, L., Wang, H., & Xie, Y. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.6.063002 · Cited by 3
Independent component analysis of DTI data reveals white matter covariances in Alzheimer's disease
Ouyang, X., Sun, X., Guo, T., Sun, Q., Chen, K., Li, Y., Wu, X., & Guo, X. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2042237 · Cited by 3
Radiomics for Alzheimer’s Disease: Fundamental Principles and Clinical Applications
Georgiadou, E., Bougias, H., Leandrou, S., & Stogiannos, N. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-31982-2_34 · Cited by 3
de Oliveira, C. E. G., de Araújo, W. M., Teixeira, A. B. M. D. J., GONÇALVES, G. B., Itikawa, E. N., & Initiative, A. D. N. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad0ddd · Cited by 3
Harnessing graphics processing units for improved neuroimaging statistics
Eklund, A., Villani, M., & LaConte, S. M. (2013) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-013-0165-7 · Cited by 3
Gemignani, J., Bayet, L., Kabdebon, C., Blankertz, B., Pugh, K. R., & Aslin, R. Ν. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512209 · Cited by 3
Cerebral metabolic covariance in delirium: pattern response to symptomatic changes
Colloby, S. J., Nitchingham, A., Richardson, S. J., Lawson, R. A., O'Brien, J. T., Wegner, E. A., Welschinger, R., Caplan, G. A., & Taylor, J. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70993 · Cited by 2
Brain patterns of pace – but not rhythm – are associated with vascular disease in older adults
Tripathi, S., Verghese, J., Callisaya, M. L., Mahoney, J. R., Srikanth, V. K., & Blumen, H. M. (2022) · Cerebral Circulation - Cognition and Behavior · doi:10.1016/j.cccb.2022.100154 · Cited by 2
Ferreira, F. S., Pereira, J., Duarte, J. V., & Castelo-Branco, M. S. (2017) · The Open Neuroimaging Journal · doi:10.2174/1874440001711010032 · Cited by 2
González, C. C. O., Ranchod, S., & Rakobowchuk, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312109 · Cited by 1
Regularized CCA identifies sex-specific brain-behavior associations in adolescent psychopathology
Milecki, L., González, C., Adeli, E., Nooner, K. B., Sabuncu, M. R., Kuceyeski, A. F., & Zhao, Q. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03678-9 · Cited by 3
Niethammer, M., & Franceschi, A. M. (2021) · Springer eBooks · doi:10.1007/978-3-030-82367-2_36 · Cited by 1
Multiscale comparative connectomics
Gopalakrishnan, V., Chung, J., Bridgeford, E., Pedigo, B. D., Arroyo, J., Upchurch, L., Johnson, G. A., Wang, N., Park, Y., Priebe, C. E., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.2 · Cited by 2
LSSL: Longitudinal Self-Supervised Learning.Preprint
Zhao, Q., Liu, Z., Adeli, E., & Pohl, K. M. (2020) · arXiv (Cornell University) · Cited by 1
Structural brain imaging and multivariate analysis enable virtual lumbar punctures
Habeck, C. G., & Whitwell, J. L. (2012) · Neurology · doi:10.1212/wnl.0b013e31827b9305
Self-citationFunctional Imaging to Study Movement Disorders
Sako, W., Uluğ, A. M., & Eidelberg, D. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-405195-9.00012-3
Brain Network Metabolic Changes in Patients with Parkinsonian Tremors
Mure, H., Eidelberg, D., & Goto, S. (2016) · InTech eBooks · doi:10.5772/63159
Weinstein, S. M., Davatzikos, C. A., Doshi, J. J., Linn, K. A., Shinohara, R. T., & Initiative, A. D. N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.27.477859
Brain signal variability during rest as a neural mechanism underlying cognitive reserve
Coors, A., Stern, Y., & Habeck, C. G. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122150
Self-citationGray Matter Covariance Networks Associated With Parental Longevity—Results From the LonGenity Study
Delpratt, N. A., Barzilai, N., Milman, S., Aleksić, S., Weiss, E. F., Verghese, J., & Blumen, H. M. (2025) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaf066
Harrison, J. R., Colloby, S. J., O'Brien, J. T., & Taylor, J. (2025) · Journal of Neurology · doi:10.1007/s00415-025-13332-5
Fujita, K., Kaneda, D., Sakurai, K., Matsubara, T., Harada, M., & Izumi, Y. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877251411404
Komaty, A. (2014) · doi:10.70675/518d478czaf46z4bebz9da9z318b56e9b82f
MacPhee, I., Goheen, J., Sekeres, M. J., Kousaie, S., & Anderson, J. A. E. (2026) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2026.06.007
Vollmer, B. L., Habeck, C. G., Kalia, V., Vardarajan, B. N., Scarmeas, N., Miller, G. W., Stern, Y., & Gu, Y. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1898605
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
