Background: Recently, an animal model for depression has shown that ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, elicits a rapid-acting antidepressant effect by blocking NMDAR-dependent bursting in the lateral habenula (Hb). However, evidence from human studies remains scarce.Methods: This study explored the changes of resting-state functional connectivity (FC) of the Hb in responders and nonresponders who was diagnosed with unipolar or bipolar depression before and after ketamine treatment. The response was defined as a ≥50% reduction in the total MADRS score at Day 13 (24 h following the sixth infusion) in comparison with the baseline score. Correlation analyses were performed to identify an association between symptom improvement and the signals of the significantly different brain regions detected in the above imaging analysis.Results: In the post-hoc region-of-interest analysis, an enhanced baseline FC between Hb and several hubs of the default mode network (including angulate cortex, precuneus, medial prefrontal cortex, and middle temporal cortex) was observed in responders (≥50% decrease in the Montgomery-Asberg Scale at 2 weeks) compared with nonresponders.Conclusions: These pilot findings may suggest a potential neural mechanism by which ketamine exerts its robust antidepressant efficacy via downregulation of aberrant habenular FC with parts of the default mode network.

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Background Microglia have been implicated in the pathogenesis of radiation-induced brain injury (RIBI), which severely influences the quality of life during long-term survival. Recently, irradiated microglia were speculated to present an aging-like phenotype. Long noncoding RNAs (lncRNAs) have been recognized to regulate a wide spectrum of biological processes, including senescence; however, their potential role in irradiated microglia remains largely uncharacterized. Methods We used bioinformatics and experimental methods to identify and analyze the senescence phenotype of irradiated microglia. Western blotting, enzyme-linked immunosorbent assays, immunofluorescence, and quantitative real-time reverse transcription-polymerase chain reaction were performed to clarify the relationship between the radiation-induced differentially expressed lncRNAs (RILs) and the distinctive molecular features of senescence in irradiated microglia. Results We found that the senescence of microglia could be induced using ionizing radiation (IR). A mutual regulation mode existed between RILs and three main features of the senescence phenotype in irradiated microglia: inflammation, the DNA damage response (DDR), and metabolism. Specifically, for inflammation, the expression of two selected RILs (ENSMUST00000190863 and ENSMUST00000130679) was dependent on the major inflammatory signaling pathways of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK). The two RILs modulated the activation of NF-κB/MAPK signaling and subsequent inflammatory cytokine secretion. For the DDR, differential severity of DNA damage altered the expression profiles of RILs. The selected RIL, ENSMUST00000130679, promoted the DDR. For metabolism, blockade of sterol regulatory element-binding protein-mediated lipogenesis attenuated the fold-change of several RILs induced by IR. Conclusions Our findings revealed that certain RILs interacted with senescence in irradiated microglia. RILs actively participated in the regulation of senescence features, suggesting that RILs could be promising intervention targets to treat RIBI.

A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known methods in this branch is distributional RL, which models return distribution instead of scalar value. In another line of work, hybrid reward architectures (HRA) in RL have studied to model source-specific value functions for each source of reward, which is also shown to be beneficial in performance. To fully inherit the benefits of distributional RL and hybrid reward architectures, we introduce Multi-Dimensional Distributional DQN (MD3QN), which extends distributional RL to model the joint return distribution from multiple reward sources. As a by-product of joint distribution modeling, MD3QN can capture not only the randomness in returns for each source of reward, but also the rich reward correlation between the randomness of different sources. We prove the convergence for the joint distributional Bellman operator and build our empirical algorithm by minimizing the Maximum Mean Discrepancy between joint return distribution and its Bellman target. In experiments, our method accurately models the joint return distribution in environments with richly correlated reward functions, and outperforms previous RL methods utilizing multi-dimensional reward functions in the control setting.