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A clustering-based method to detect functional connectivity differences.

ABSTRACT: Recently, resting-state functional magnetic resonance imaging (R-fMRI) has emerged as a powerful tool for investigating functional brain organization changes in a variety of neurological and psychiatric disorders. However, the current techniques may need further development to better define the reference brain networks for quantifying the functional connectivity differences between normal and diseased subject groups. In this study, we introduced a new clustering-based method that can clearly define the reference clusters. By employing group difference information to guide the clustering, the voxels within the reference clusters will have homogeneous functional connectivity changes above predefined levels. This method identified functional clusters that were significantly different between the amnestic mild cognitively impaired (aMCI) and age-matched cognitively normal (CN) subjects. The results indicated that the distribution of the clusters and their functionally disconnected regions resembled the altered memory network regions previously identified in task fMRI studies. In conclusion, the new clustering method provides an advanced approach for studying functional brain organization changes associated with brain diseases.

SUBMITTER: Chen G

PROVIDER: S-EPMC3342474 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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A clustering-based method to detect functional connectivity differences.

Chen Gang G Ward B Douglas BD Xie Chunming C Li Wenjun W Chen Guangyu G Goveas Joseph S JS Antuono Piero G PG Li Shi-Jiang SJ

NeuroImage 20120303 1

Recently, resting-state functional magnetic resonance imaging (R-fMRI) has emerged as a powerful tool for investigating functional brain organization changes in a variety of neurological and psychiatric disorders. However, the current techniques may need further development to better define the reference brain networks for quantifying the functional connectivity differences between normal and diseased subject groups. In this study, we introduced a new clustering-based method that can clearly def ...[more]

PMID: 22405733

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A clustering-based method to detect functional connectivity differences.

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A clustering-based method to detect functional connectivity differences.

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