Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder.

INTRODUCTION: The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders. METHODS: We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition. RESULTS: We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD. CONCLUSIONS: Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.

Structural connectivity between the hippocampus and cortical/subcortical area relates to cognitive impairment in schizophrenia but not in mood disorders.

Cognitive impairment in schizophrenia and other psychiatric disorders is a challenge to be overcome in order to maintain patients' quality of life and social function. The neurological pathogenesis of cognitive impairment requires further elucidation. In general, the hippocampus interacts between the cortical and subcortical areas for information processing and consolidation and has an important role in memory. We examined the relationship between structural connectivity of the hippocampus and cortical/subcortical areas and cognitive impairment in schizophrenia, major depressive disorder and bipolar disorder. Subjects comprised 21 healthy controls, 19 patients with schizophrenia, 20 patients with bipolar disorder and 18 patients with major depressive disorder. Diffusion-weighted tensor images data were processed using ProbtrackX2 to calculate the structural connectivity between the hippocampus and cortical/subcortical areas. Cognitive function was assessed using the Brief Assessment of Cognition in schizophrenia composite score. Hippocampal structural connectivity index was significantly correlated with composite score in the schizophrenia group but not in the healthy control, major depressive disorder or bipolar disorder groups. There were no statistically significant differences in hippocampal structural connectivity index between the four groups. Structural connectivity between the hippocampus and cortical/subcortical areas is suggested to be a pathophysiological mechanism of comprehensive cognitive impairment in schizophrenia.

The Big Five personality traits and the fear of COVID-19 in predicting depression and anxiety among Japanese nurses caring for COVID-19 patients: A cross-sectional study in Wakayama prefecture.

Recent studies have found a relationship between fear of COVID-19 and mental health problems. Medical workers caring for COVID-19 patients tend to suffer from mental health problems; however, the impact of their personality traits, in the form of mental problems like depression and anxiety in Japan is unclear. In this study, we investigated the risk of nurses' depression and anxiety, predicted by the fear of COVID-19 and the Big Five personality traits. A total of 417 nurses working in hospitals providing care to COVID-19 patients in Wakayama prefecture of the Kansai region participated in this study. The questionnaires comprised items on nurses' basic characteristics and three scales: the Fear of COVID-19 Scale 2020, the Big-Five Scale, and the Japanese version of the Hospital Anxiety and Depression Scale (HADS). Depression and anxiety in the HADS were set as dependent variables, and basic attributes, fear, and personality traits as independent variables; multivariate logistic regression analyses were conducted. The questionnaire, with no missing items was distributed from February to March 2021. Neuroticism (OR = 1.06, 95%CI = 1.03-1.09) was the only significant factor associated with the depression symptom, and both FCV-19S scores (OR = 1.16, 95%CI = 1.09-1.23) and neuroticism (OR = 1.09, 95%CI = 1.06-1.13) were the significant factors associated with anxiety. The Nagelkerke's R squared was 0.171 in the depression model and 0.366 in the anxiety model. Thus, it was found that it is necessary to support nurses' mental health by developing methods suitable to their personalities.