Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression.

BACKGROUND: Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear. METHODS: This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design. RESULTS: Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups. CONCLUSIONS: Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.

Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.

Tris(1,3-dichloro-2-propyl)phosphate Reduces Growth Hormone Expression via Binding to Growth Hormone Releasing Hormone Receptors and Inhibits the Growth of Crucian Carp.

Tris(1,3-dichloro-2-propyl)phosphate (TDCIPP) has commonly been used as an additive flame retardant and frequently detected in the aquatic environment and in biological samples worldwide. Recently, it was found that exposure to TDCIPP inhibited the growth of zebrafish, but the relevant molecular mechanisms remained unclear. In this study, 5 day-old crucian carp (Carassius auratus) larvae were treated with 0.5, 5, or 50 µg/L TDCIPP for 90 days; the effect on growth was evaluated; and related molecular mechanisms were explored. Results demonstrated that 5 or 50 µg/L TDCIPP treatment significantly inhibited the growth of crucian carp and downregulated the expression of growth hormones (ghs), growth hormone receptor (ghr), and insulin-like growth factor 1 (igf1). Molecular docking, dual-luciferase reporter gene assay, and in vitro experiments demonstrated that TDCIPP could bind to the growth hormone releasing hormone receptor protein of crucian carp and disturb the stimulation of growth hormone releasing hormone to the expression of ghs, resulting in the decrease of the mRNA level of gh1 and gh2 in pituitary cells. Our findings provide new perceptions into the molecular mechanisms of developmental toxicity of TDCIPP in fish.

A comprehensive system for detection of behavioral change of D. magna exposed to various chemicals.

The purpose of the present study was to develop a sensitive and comprehensive method, based on D. magna swimming behavior, for toxicity assessment of environmental chemicals. Firstly, D. magna swimming in several chambers with different diameters were compared to determine the most suitable container, and then baseline behaviors during light/dark periods as well as reactions to light/dark switching and vibration stimulation were determined. Secondly, after exposure to sub-lethal concentrations of the selected 42 typical chemicals, which were classified into heavy metals, pesticides, fungicides and flame retardants, the alterations in the swimming parameters were evaluated. Our results indicated the 48-well plate was the most suitable chamber for behavioral monitoring of D. magna, and specific responsive patterns of D. magna neonates to light/dark switching and vibration stimulation were observed. The results of the behavioral assays of chemicals suggested that D. magna was the most sensitive to methylmercury-chloride and then to abamectin and chlorpyrifos. The three chemicals at several to dozens of ng/L significantly changed swimming behaviors of D. magna. Furthermore, the alteration in the behavioral parameters (average swimming speed, etc.) induced by the selected chemicals could be ascribed to various modes of actions, confirming the reliability and practicability of the monitoring method.