The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics.

<bold>Background: </bold>From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies.<bold>Methods: </bold>Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis.<bold>Results: </bold>Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively).<bold>Conclusion: </bold>Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration.<bold>Limitations: </bold>Included number of trials was low and heterogeneity should be noted. [ABSTRACT FROM AUTHOR]