Resting network is composed of more than one neural pattern: an fMRI study

Abstract

In resting state, the dynamics of blood oxygen level-dependent signals recorded by functional magnetic resonance imaging (fMRI) showed reliable modular structures. To explore the network property, previous research used to construct an adjacency matrix by Pearson's correlation and prune it using stringent statistical threshold. However, traditional analyses may lose useful information at middle to moderate high correlation level. This resting fMRI study adopted full connection as a criterion to partition the adjacency matrix into composite sub-matrices (neural patterns) and investigated the associated community organization and network features. Modular consistency across subjects was assessed using scaled inclusivity index. Our results disclosed two neural patterns with reliable modular structures. Concordant with the results of traditional intervention, community detection analysis showed that neural pattern 1, the sub-matrix at highest correlation level, was composed of sensory-motor, visual associative, default mode/midline, temporal limbic and basal ganglia structures. The neural pattern 2 was situated at middle to moderate high correlation level and comprised two larger modules, possibly associated with mental processing of outer world (such as visuo-associative, auditory and sensory-motor networks) and inner homeostasis (such as default-mode, midline and limbic systems). Graph theoretical analyses further demonstrated that the network feature of neural pattern 1 was more local and segregate, whereas that of neural pattern 2 was more global and integrative. Our results suggest that future resting fMRI research may take the neural pattern at middle to moderate high correlation range into consideration, which has long been ignored in extant literature. The variation of neural pattern 2 could be relevant to individual characteristics of self-regulatory functions, and the disruption in its topology may underlie the pathology of several neuropsychiatric illnesses.

Keywords: community detection; functional connectivity; functional magnetic resonance imaging (fMRI); graph theory; resting fMRI; scaled inclusivity.