Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning.

Purpose: This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data.

Methods: We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features.

A two-branch trade-off neural network for balanced scoring sleep stages on multiple cohorts.

Introduction: Automatic sleep staging is a classification process with severe class imbalance and suffers from instability of scoring stage N1. Decreased accuracy in classifying stage N1 significantly impacts the staging of individuals with sleep disorders. We aim to achieve automatic sleep staging with expert-level performance in both N1 stage and overall scoring.

Efficacy of transcutaneous electrical acupoint stimulation for patients with first-episode schizophrenia: An 8-week, preliminary, randomized controlled trial.

Combination therapy with antipsychotics has been investigated for treating schizophrenia, and has shown clear advantages among non-invasive therapies. Transcutaneous electrical acupoint stimulation (TEAS) is a novel non-invasive treatment with definite efficacy in treating mental disorders. The current study aimed to investigate the efficacy of TEAS in further improving the psychotic symptoms in patients with first-episode schizophrenia (FES) being treated with pharmacological drugs.

Assessing the Effect of Simultaneous Combining of Transcranial Direct Current Stimulation and Transcutaneous Auricular Vagus Nerve Stimulation on the Improvement of Working Memory Performance in Healthy Individuals.

A previous study found that combining transcranial direct current stimulation (tDCS) and transcutaneous auricular vagus nerve stimulation (taVNS) could evoke significantly larger activation on a range of cortical and subcortical brain regions than the numerical summation of tDCS and taVNS effects. In this study, two within-subject experiments were employed to investigate its effects on working memory (WM). In experiment 1, the WM modulatory effects of tDCS over the left dorsolateral prefrontal cortex (DLPFC), taVNS, and simultaneous joint simulation of tDCS over the left DLPFC and taVNS (SJS-L) were compared among 60 healthy subjects.

Transcutaneous Auricular Vagus Nerve Stimulation Improves Spatial Working Memory in Healthy Young Adults.

Working memory (WM) is one of the core components of higher cognitive functions. There exists debate regarding the extent to which current techniques can enhance human WM capacity. Here, we examined the WM modulation effects of a previously less studied technique, transcutaneous auricular vagus nerve stimulation (taVNS).

The Feature of Sleep Spindle Deficits in Patients With Schizophrenia With and Without Auditory Verbal Hallucinations.

Background: Previous sleep electroencephalography studies have detected abnormalities in sleep architecture and sleep spindle deficits in schizophrenia (SCZ), but the consistency of these results was not robust, which might be due to the small sample size and the influence of clinical factors such as the various medication therapies and symptom heterogeneity. This study aimed to regard auditory verbal hallucinations (AVHs) as a pointcut to downscale the heterogeneity of SCZ and explore whether some sleep architecture and spindle parameters were more severely impaired in SCZ patients with AVHs compared with those without AVHs.

Methods: A total of 90 SCZ patients with AVHs, 92 SCZ patients without AVHs, and 91 healthy control subjects were recruited, and parameters of sleep architecture and spindle activities were compared between groups.

A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic.

Background: Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control.