论文信息 - Association of resting-state network dysfunction with their dynamics of inter-network interactions in depression.

Association of resting-state network dysfunction with their dynamics of inter-network interactions in depression.

BACKGROUND Network-level brain analysis on resting state has demonstrated that depression is not only associated with intra-network dysfunction, but relates to the disturbed interplay between the networks. However, the underlying associations between the intra-network dysfunction and the disturbed inter-network interactions remain unexplored. This study was aimed to explore the association of resting-state networks dysfunction with their dynamics of inter-network interactions in depression. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were collected from 20 depressed patients and 20 matched healthy controls. We evaluated the Hurst exponents of the time series from resting-state networks, and employed multivariate pattern analysis to capture depression-associated networks with increased or decreased Hurst values. Granger causalities between these networks were explored to undertake an intensive study of the dynamic inter-network interactions. RESULTS The default mode network (DMN) exhibited decreased Hurst value, indicative of more irregular oscillation within the DMN implicated in depressive symptoms. The ventromedial prefrontal network (vmPFN) and salience network (SN) with increased Hurst values, as compensatory mechanisms, continually enhanced the interactions to the DMN for trying hard to impel the DMN to function synchronously. On the other side, the DMN exerted frequently enhanced causality on the left fronto-parietal network with elevated Hurst exponent, accompanied by imbalance between the fronto-parietal network and DMN circuits in depression. LIMITATIONS This study suffers from small sample size and is confined to large-scale networks. CONCLUSIONS Our preliminary findings mainly revealed the DMN-related dynamic interactions with the vmPFN, SN and the fronto-parietal network in depression, which might offer useful information for discovering the neuropathological mechanisms underlying the depressive symptoms.

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