Distance-based analysis of variance for brain connectivity
Open accessAbstract
The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder‐related changes. Unfortunately, classical statistical testing procedures are not well suited to high‐dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low‐dimensional features, a process that might mask the salient features of high‐dimensional distributions. In this work, we consider a general framework for two‐sample testing of complex structures by studying generalized within‐group and between‐group variances based on distances between complex and potentially high‐dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.
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Similarity-based multimodal regression
Chen, A. A., Weinstein, S. M., Adebimpe, A., Gur, R. C., Gur, R. E., Merikangas, K. R., Satterthwaite, T. D., Shinohara, R. T., & Shou, H. (2024) · Biostatistics · doi:10.1093/biostatistics/kxad033 · Cited by 6 · see citing works
Self-citation
- Authors
- Shinohara, R. T., Shou, H., Carone, M., Schultz, R., Tunc, B., Parker, D., Martin, M. L., & Verma, R.
- Affiliations
- University of Pennsylvania (US)
- University of Washington (US)
- Children's Hospital of Philadelphia (US)
- Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania
- Corresponding author
- Shinohara, Russell T.
- ORCID
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-09-30
- Volume
- 76
- Issue
- 1
- Pages
- 257-269
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- PMC
- PMC7653688
- OpenAlex
- W2548870620
- Cited by
- 6
- References
- 39
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2019: 1
- FWCI
- 0.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- brain connectivity, structural connectivity, autism spectrum disorder, two-sample test, high-dimensional data, complex-valued data, asymptotic null distribution, neuroimaging
- MeSH terms
- Autism Spectrum Disorder, Adolescent, Analysis of Variance, Biometry, Child, Computer Simulation, Data Interpretation, Statistical, Humans, Diffusion Tensor Imaging, Connectome
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