Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

The growing impact of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection. Per- and poly-fluoroalkyl substances (PFASs) are manufactured fluorinated chemicals widely used in many industrial and household products. The objective of this case-control study was to assess the association between PFASs exposure and COVID-19 susceptibility and to elucidate the metabolic dysregulation associated with PFASs exposure in COVID-19 patients. Total 160 subjects (80 COVID-19 patients and 80 symptom-free controls) were recruited from Shanxi and Shandong provinces, two regions heavily polluted by PFASs in China. Twelve common PFASs were quantified in both urine and serum. Urine metabolome profiling was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). In unadjusted models, the risk of COVID-19 infection was positively associated with urinary levels of perfluorooctanesulfonic acid (PFOS) (Odds ratio: 2.29 [95% CI: 1.52–3.22]), perfluorooctanoic acid (PFOA) (2.91, [1.95–4.83], and total PFASs (∑ (12) PFASs) (3.31, [2.05–4.65]). After controlling for age, sex, body mass index (BMI), comorbidities, and urine albumin-to-creatinine ratio (UACR), the associations remained statistically significant (Adjusted odds ratio of 1.94 [95% CI: 1.39–2.96] for PFOS, 2.73 [1.71–4.55] for PFOA, and 2.82 [1.97–3.51] for ∑ (12) PFASs). Urine metabolome-PFASs association analysis revealed that 59% of PFASs-associated urinary endogenous metabolites in COVID-19 patients were identified to be produced or largely regulated by mitochondrial function. In addition, the increase of PFASs exposure was associated with the accumulation of key metabolites in kynurenine metabolism, which are involved in immune responses (Combined β coefficient of 0.60 [95% CI: 0.25–0.95, P = 0.001]). Moreover, alternations in PFASs-associated metabolites in mitochondrial and kynurenine metabolism were also correlated with clinical lab biomarkers for mitochondrial function (serum growth/differentiation factor-15) and immune activity (lymphocyte percentage), respectively. Elevated exposure to PFASs was independently associated with an increased risk of COVID-19 infection. PFASs-associated metabolites were implicated in mitochondrial function and immune activity. Larger studies are needed to confirm our findings and further understand the underlying mechanisms of PFASs exposure in the pathogenesis of SARS-CoV2 infection.

There have been many neuroimaging studies of human personality traits, and it have already provided glimpse into the neurobiology of complex traits. And most of previous studies adopt voxel-based morphology (VBM) analysis to explore the brain-personality mechanism from two levels (vertex and regional based), the findings are mixed with great inconsistencies and the brain-personality relations are far from a full understanding. Here, we used a new method of surface-based morphology (SBM) analysis, which provides better alignment of cortical landmarks to generate about the associations between cortical morphology and the personality traits across 120 healthy individuals at both vertex and regional levels. While to further reveal local functional correlates of the morphology-personality relationships, we related surface-based functional homogeneity measures to the regions identified in the regional-based SBM correlation. Vertex-wise analysis revealed that people with high agreeableness exhibited larger areas in the left superior temporal gyrus. Based on regional parcellation we find that extroversion was negatively related with the volume of the left lateral occipito-temporal gyrus and agreeableness was negatively associated with the sulcus depth of the left superior parietal lobule. Moreover, increased regional homogeneity in the left lateral occipito-temporal gyrus is related to the scores of extroversion，and increased regional homogeneity in the left superior parietal lobule is related to the scores of agreeableness. These findings provide supporting evidence of a link between personality and brain structural mysteries with a method of SBM, and further suggest that local functional homogeneity of personality traits has neurobiological relevance that is likely based on anatomical substrates.

Background and objective: Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves’ disease (GD). However, many patients will develop hypothyroidism as early as 6 months after RAI. This study aimed to implement machine learning (ML) algorithms for the early prediction of post-RAI hypothyroidism. Methods: Four hundred and seventy-one GD patients who underwent RAI between January 2016 and June 2019 were retrospectively recruited and randomly split into the training set (310 patients) and the validation set (161 patients). These patients were followed for 6 months after RAI. A set of 138 clinical and lab test features from the electronic medical record (EMR) were extracted, and multiple ML algorithms were conducted to identify the features associated with the occurrence of hypothyroidism 6 months after RAI. Results: An integrated multivariate model containing patients’ age, thyroid mass, 24-h radioactive iodine uptake, serum concentrations of aspartate aminotransferase, thyrotropin-receptor antibodies, thyroid microsomal antibodies, and blood neutrophil count demonstrated an area under the receiver operating curve (AUROC) of 0.72 (95% CI: 0.61–0.85), an F1 score of 0.74, and an MCC score of 0.63 in the training set. The model also performed well in the validation set with an AUROC of 0.74 (95% CI: 0.65–0.83), an F1 score of 0.74, and a MCC of 0.63. A user-friendly nomogram was then established to facilitate the clinical utility. Conclusion: The developed multivariate model based on EMR data could be a valuable tool for predicting post-RAI hypothyroidism, allowing them to be treated differently before the therapy. Further study is needed to validate the developed prognostic model at independent sites.

Background and Aims: Adolescent major depressive disorder (MDD) is prevalent globally but often goes unnoticed due to differences in symptoms compared to adult criteria. Analyzing the brain from a network perspective provides new insights into higher‐level brain functions and its pathophysiology. This study aimed to investigate changes in the topological organization of functional networks in adolescents with first‐episode, treatment‐naïve MDD.Method: The study included 23 adolescents with depression and 27 matched healthy controls (HCs). Resting‐state functional MRI (rs‐fMRI) was conducted, and whole‐brain functional networks were constructed. Graph theory analysis was used to evaluate network topological properties. A machine‐learning multivariate diagnostic model was developed using network metrics associated with depression severity.Results: Both the MDD and HC groups displayed small‐world topology, with male MDD patients showing reduced global clustering efficiency (Cp). The nodal Cp (NCp) and local efficiency (NLE) in the bilateral pallidum were significantly positively correlated with depression severity. In contrast, nodal efficiency (NE) in the left medial orbital superior frontal gyri (ORBsupmed) showed a negative correlation with disease severity. A machine‐learning multivariate model using regional network topological features produced an AUROC of 0.71 (95% CI: 0.54–0.92) and an F1 score of 0.65, successfully differentiating adolescent MDD from HCs.Conclusion: Our findings suggest disruptions in small‐world topology in both global and local brain networks in adolescent depression. These abnormal nodal properties may serve as novel neural markers of the disorder.