Cluster-based statistics for brain connectivity in correlation with behavioral measures.
Open accessAbstract
Graph theoretical approaches have successfully revealed abnormality in brain connectivity, in particular, for contrasting patients from healthy controls. Besides the group comparison analysis, a correlational study is also challenging. In studies with patients, for example, finding brain connections that indeed deepen specific symptoms is interesting. The correlational study is also beneficial since it does not require controls, which are often difficult to find, especially for old-age patients with cognitive impairment where controls could also have cognitive deficits due to normal ageing. However, one of the major difficulties in such correlational studies is too conservative multiple comparison correction. In this paper, we propose a novel method for identifying brain connections that are correlated with a specific cognitive behavior by employing cluster-based statistics, which is less conservative than other methods, such as Bonferroni correction, false discovery rate procedure, and extreme statistics. Our method is based on the insight that multiple brain connections, rather than a single connection, are responsible for abnormal behaviors. Given brain connectivity data, we first compute a partial correlation coefficient between every edge and the behavioral measure. Then we group together neighboring connections with strong correlation into clusters and calculate their maximum sizes. This procedure is repeated for randomly permuted assignments of behavioral measures. Significance levels of the identified sub-networks are estimated from the null distribution of the cluster sizes. This method is independent of network construction methods: either structural or functional network can be used in association with any behavioral measures. We further demonstrated the efficacy of our method using patients with subcortical vascular cognitive impairment. We identified sub-networks that are correlated with the disease severity by exploiting diffusion tensor imaging techniques. The identified sub-networks were consistent with the previous clinical findings having valid significance level, while other methods did not assert any significant findings.
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Permutation-based NBS and CBS were proposed to cope with the problem of multiple comparison while preventing too conservative correction in connectivity analysis using group differences or correlation coefficients, respectively.
“Permutation-based network-based statistics (NBS) (Zalesky et al., 2010) and cluster-based statistics (CBS) (Han et al., 2013) were performed for each process, which were proposed to cope with the problem of multiple comparison while preventing too conservative correction in connectivity analysis using group differences or correlation coefficients, respectively.”
From Effects of Alzheimer’s and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer’s Disease (2021) · cites this paper as Han et al., 2013
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Flagged first, then by citations
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- Authors
- Han, C. E., Yoo, S. W., Seo, S. W., Na, D. L., & Seong, J.-K.
- Affiliations
- Korea University (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Samsung Medical Center (KR)
- Corresponding author
- Seong, Joon-Kyung
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-08-19
- Volume
- 8
- Issue
- 8
- Pages
- e72332
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3747142
- OpenAlex
- W2055978803
- Cited by
- 49
- References
- 105
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 2 · 2017: 4 · 2016: 4 · 2015: 5 · 2014: 1 · 2013: 1
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Research Foundation (2010-0028631)
- Ministry of Science, ICT and Future Planning (2010‐0028631)
- National Research Foundation of Korea (2010-0028631, No. 2010-0028631)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- brain connectivity, partial correlation, multiple testing correction, graph theory, subcortical vascular cognitive impairment, diffusion tensor imaging, disease severity, behavioral measures, subnetworks
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Psychomotor Performance, Severity of Illness Index, Dementia, Vascular, Cluster Analysis, Diffusion Tensor Imaging, Cognitive Dysfunction
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