Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.

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This retrospective study indicates that only approximately 50% of genotyped patients with hypokalemic periodic paralysis respond to acetazolamide. We found evidence supporting a relationship between genotype and treatment response. Prospective randomized controlled trials are required to further evaluate this relationship. Development of alternative therapies is required.

Whether baseline metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) measured by FDG-PET/CT affected prognosis of patients with lymphoma was controversial. We searched PubMed, EMBASE and Cochrane to identify studies assessing the effect of baseline TMTV and TLG on the survival of lymphoma patients. Pooled hazard ratios (HR) for overall survival (OS) and progression-free survival (PFS) were calculated, along with 95% confidence intervals (CI). Twenty-seven eligible studies including 2,729 patients were analysed. Patients with high baseline TMTV showed a worse prognosis with an HR of 3.05 (95% CI 2.55–3.64, p<0.00001) for PFS and an HR of 3.07 (95% CI 2.47–3.82, p<0.00001) for OS. Patients with high baseline TLG also showed a worse prognosis with an HR of 3.44 (95% CI 2.37–5.01, p<0.00001) for PFS and an HR of 3.08 (95% CI 1.84–5.16, p<0.00001) for OS. A high baseline TMTV was significantly associated with worse survival in DLBCL patients treated with R-CHOP (OS, pooled HR = 3.52; PFS, pooled HR = 2.93). A high baseline TLG was significantly associated with worse survival in DLBCL patients treated with R-CHOP (OS, pooled HR = 3.06; PFS, pooled HR = 2.93). The negative effect of high baseline TMTV on PFS was demonstrated in HL (pooled HR = 3.89). A high baseline TMTV was significantly associated with worse survival in ENKL patients (OS, pooled HR = 2.24; PFS, pooled HR = 3.25). A high baseline TLG was significantly associated with worse survival in ENKL patients (OS, pooled HR = 2.58; PFS, pooled HR = 2.99). High baseline TMTV or TLG predict significantly worse PFS and OS in patients with lymphoma. Future studies are warranted to explore whether TMTV or TLG could be integrated into various prognostic models for clinical decision making.