Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional
networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't
separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach
based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction
between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less
task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast
FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting
condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group
ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs
significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial
correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences.
Our results provide new evidences for the disconnection hypothesis in AD.

Functional network connectivity analysis based on partial correlation in Alzheimer's disease

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Functional network connectivity analysis based on partial correlation in Alzheimer's disease