OBJECTIVE

In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections.

MATERIALS AND METHODS

We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time-frequency analysis, and (c) HC-SP group differences in functional network connections and in task-modulation of these connections.

RESULTS

This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual-frontal, medial temporal-medial visual, parietal-medial temporal, parietal-medial visual and medial temporal-anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)-orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor-frontal, RLFP-medial temporal and posterior default mode (pDM)-parietal connections were significantly greater in SP, and task modulation of orbitofrontal-pDM and medial temporal-frontal connections were significantly greater in HC (all P < 0.05).

CONCLUSION

The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.