Cluster-based statistics for brain connectivity in correlation with behavioral measures

Cheol E. Han, Sang Wook Yoo, Sang Won Seo, Duk L. Na, Jun Kyung Seong

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere72332
JournalPLoS One
Volume8
Issue number8
DOIs
Publication statusPublished - 2013 Aug 19

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Brain
statistics
Statistics
brain
Diffusion tensor imaging
methodology
abnormal behavior
Aging of materials
Diffusion Tensor Imaging
Imaging techniques
blood vessels
disease severity
signs and symptoms (animals and humans)
Blood Vessels
image analysis

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Cluster-based statistics for brain connectivity in correlation with behavioral measures. / Han, Cheol E.; Yoo, Sang Wook; Seo, Sang Won; Na, Duk L.; Seong, Jun Kyung.

In: PLoS One, Vol. 8, No. 8, e72332, 19.08.2013.

Research output: Contribution to journalArticle

Han, Cheol E. ; Yoo, Sang Wook ; Seo, Sang Won ; Na, Duk L. ; Seong, Jun Kyung. / Cluster-based statistics for brain connectivity in correlation with behavioral measures. In: PLoS One. 2013 ; Vol. 8, No. 8.
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