Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional magnetic resonance imaging (fMRI) and graph theory to examine the whole-brain functional networks among 42 MDD patients and 42 healthy controls. Our results showed that compared with healthy controls, MDD patients showed higher local efficiency and modularity. Furthermore, MDD patients showed altered nodal centralities of many brain regions, including hippocampus, temporal cortex, anterior cingulate gyrus and dorsolateral prefrontal gyrus, mainly located in default mode network and cognitive control network. Together, our results suggested that MDD was associated with disruptions in the topological structure of functional brain networks, and provided new insights concerning the pathophysiological mechanisms of MDD.

BackgroundMajor depressive disorder (MDD) is associated with dysfunction between cognitive control and affective processing system. However, little is known about alterations of the nodal and edge efficiency in abnormal systems of MDD patients. We used two independent datasets and two different structural templates to investigate the alterations of the nodal and edge efficiency of whole-brain functional networks of MDD.MethodForty-two MDD and forty-two age, education-matched controls were selected to investigate network efficiency abnormalities of the MDD patients’ cortical and subcortical regions, as well as the disrupted functional connectivity between these regions, from the perspective of network topological architectures. In addition, another dataset, which included thirty MDD patients and thirty controls, was also investigated using the same method.ResultsResults showed that MDD group demonstrated significant increase in the local efficiency, although not change of global efficiency. In addition, nodal efficiency was found to increase in affective processing regions (i.e., amygdale, thalamus, hippocampus), but decrease in cognitive control related regions, which included dorsolateral prefrontal cortex and anterior cingulate cortex. The edge efficiency was found to increase, involving both connectivity between thalamus and limbic system regions and connectivity between hippocampus and regions (i.e., amygdala, thalamus). More important, result was replicated within independent datasets for the first and different structural templates for another.ConclusionsOur results indicated that MDD was associated with disrupted functional connectivity networks between cognitive control and affective processing systems. The findings might shed light on the pathological mechanism of depression and provide potential biomarkers for clinic treatment of depression.Electronic supplementary materialThe online version of this article (doi:10.1186/s12888-016-1053-9) contains supplementary material, which is available to authorized users.

This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during 1980-2010s, leading to two scenarios of forest loss, smaller but spatially continuous in Scenario 1 and larger but spatially scattered in Scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in Scenario 2 than Scenario 1; coarse resolution data and models (such as in Scenarios 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.

Net primary productivity (NPP) is an essential indicator of ecosystem function and sustainability and plays a vital role in the carbon cycle, especially in arid and semiarid grassland ecosystems. Quantifying trends in NPP and identifying the contributing factors are important for understanding the relative impacts of climate change and human activities on grassland degradation. For our case-study of Kyrgyzstan, we quantified from 2000 to 2014 the spatial and temporal patterns in climate-driven potential NPP (NPP P) using the Zhou Guangsheng model specifically developed for Asian grasslands, and actual NPP (NPP A) using the globally calibrated MOD17A3 NPP data product. By calculating the difference between NPP P and NPP A , we inferred human-induced NPP (NPP H) and thereby characterized changes in grassland NPP attributable to anthropogenic activities. The results showed that grassland NPP A in Kyrgyzstan experienced a slight decrease over time at an average rate of −0.87 g CÁm −2 Áyr −1 but patterns varied between provinces. Nearly 60% of Kyrgyzstan's grass

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