Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students

The subcortical basis of subjective sleep quality

Study objectives

To assess the association between self-reported sleep quality and cortical and subcortical local morphometry.

Methods

Sleep and neuroanatomical data from the full release of the young adult Human Connectome Project dataset were analyzed. Sleep quality was operationalized with the Pittsburgh Sleep Quality Index (PSQI). Local cortical and subcortical morphometry was measured with subject-specific segmentations resulting in voxelwise thickness measurements for cortex and relative (i.e., cross-sectional) local atrophy measurements for subcortical regions.

Results

Relative atrophy across several subcortical regions, including bilateral pallidum, striatum, and thalamus, was negatively associated with both global PSQI score and sub-components of the index related to sleep duration, efficiency, and quality. Conversely, we found no association between cortical morphometric measurements and self-reported sleep quality.

Conclusions

This work shows that subcortical regions such as the bilateral pallidum, thalamus, and striatum, might be interventional targets to ameliorate self-reported sleep quality.

Reliability of Spectral Features of Resting-State Brain Activity: A Magnetoencephalography Study

Background Cognition is a vital sign and its deterioration is a major concern in clinical medicine. It is usually evaluated using neuropsychological assessments, which have innate limitations such as the practice effect. To compensate for these assessments, the oscillatory power of resting-state brain activity has recently become available. The power is obtained noninvasively using magnetoencephalography and is summarized by spectral parameters such as the median frequency (MF), individual alpha frequency (IAF), spectral edge frequency 95 (SEF95), and Shannon's spectral entropy (SSE). As these parameters are less sensitive to practice effects, they are suitable for longitudinal studies. However, their reliability remains unestablished, hindering their proactive use in clinical practice. Therefore, we aimed to quantify the within-participant reliability of these parameters using repeated measurements of healthy participants to facilitate their clinical use and to evaluate the observed changes/differences in these parameters reported in previous studies. Methodology Resting-state brain activity with eyes closed was recorded using magnetoencephalography for five minutes from 15 healthy individuals (29.3 ± 4.6 years old: ranging from 23 to 28 years old). The following four spectral parameters were calculated: MF, IAF, SEF95, and SSE. To quantify reliability, the minimal detectable change (MDC) and intraclass correlation coefficient (ICC) were computed for each parameter. In addition, we used MDCs to evaluate the changes and differences in the spectral parameters reported in previous longitudinal and cross-sectional studies. Results The MDC at 95% confidence interval (MDC95) of MF, IAF, SEF95, and SSE were 0.61 Hz, 0.44 Hz, 2.91 Hz, and 0.028, respectively. The ICCs of these parameters were 0.96, 0.92, 0.94, and 0.83, respectively. The MDC95 of these parameters was smaller than the mean difference in the parameters between cognitively healthy individuals and patients with dementia, as reported in previous studies. Conclusions The spectral parameter changes/differences observed in prior studies were not attributed to measurement errors but rather reflected genuine effects. Furthermore, all spectral parameters exhibited high ICCs (>0.8), underscoring their robust within-participant reliability. Our results support the clinical use of these parameters, especially in the longitudinal monitoring and evaluation of the outcomes of interventions.

ERP-WGAN: A Data Augmentation Method for EEG Single-trial Detection

Brain computer interaction based on EEG presents great potential and becomes the research hotspots. However, the insufficient scale of EEG database limits the BCI system performance, especially the positive and negative sample imbalance caused by oddball paradigm. To alleviate the bottleneck problem of scarce EEG sample, we propose a data augmentation method based on generative adversarial network to improve the performance of EEG signal classification. Taking the characteristics of EEG into account in wasserstein generative adversarial networks (WGAN), the problems of model collapse and poor quality of artificial data were solved by using resting noise, smoothing and random amplitude. The quality of artificial data was comprehensively evaluated from verisimilitude, diversity and accuracy. Compared with the three artificial data methods and two data sampling methods, the proposed ERP-WGAN framework significantly improve the performance of both subject and general classifiers, especially the accuracy of general classifiers trained by less than 5 dimensional features is improved by 20-25%. Moreover, we evaluate the training sets performance with different mixing ratios of artificial and real samples. ERP-WGAN can reduced at least 73% of the real subject data and acquisition cost, which greatly saves the test cycle and research cost.

Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response

A stress group should be subdivided into eustress (low-stress) and distress (high-stress) groups to better evaluate personal cognitive abilities and mental/physical health. However, it is challenging because of the inconsistent pattern in brain activation. We aimed to ascertain the necessity of subdividing the stress groups. The stress group was screened by salivary alpha-amylase (sAA) and then, the brain’s hemodynamic reactions were measured by functional near-infrared spectroscopy (fNIRS) based on the near-infrared biosensor. We compared the two stress subgroups categorized by sAA using a newly designed emotional stimulus-response paradigm with an international affective picture system (IAPS) to enhance hemodynamic signals induced by the target effect. We calculated the laterality index for stress (LIS) from the measured signals to identify the dominantly activated cortex in both the subgroups. Both the stress groups exhibited brain activity in the right frontal cortex. Specifically, the eustress group exhibited the largest brain activity, whereas the distress group exhibited recessive brain activity, regardless of positive or negative stimuli. LIS values were larger in the order of the eustress, control, and distress groups; this indicates that the stress group can be divided into eustress and distress groups. We built a foundation for subdividing stress groups into eustress and distress groups using fNIRS.

Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: a cross-lagged panel analysis

BACKGROUND

Cross-sectional and longitudinal studies have found that problematic mobile phone use, bedtime procrastination, sleep quality, and depressive symptoms are strongly associated. However, studies are inconsistent regarding whether problematic mobile phone use predicts depressive symptoms or vice versa, and sleep factors have been infrequently focused on in this regard. In addition, few studies have examined the longitudinal associations and directions of effects between these factors. Therefore, this study aims to explore the longitudinal relationship among problematic mobile phone use, bedtime procrastination, sleep quality, and depressive symptoms in college students.

METHODS

Overall, 1181 college students completed questionnaires on problematic mobile phone use, bedtime procrastination, sleep quality, and depressive symptoms at two time points 12 months apart. A cross-lagged model was used to examine the longitudinal relationship between these factors.

RESULTS

Cross-lagged analyses showed significant bidirectional relationships of problematic mobile phone use with bedtime procrastination and depressive symptoms. Additionally, there were also significant bidirectional relationships of sleep quality with bedtime procrastination and depressive symptoms. Problematic mobile phone use predicted subsequent sleep quality one-way, and bedtime procrastination predicted subsequent depressive symptoms one-way.

CONCLUSIONS

This study further expands our understanding of the longitudinal and bidirectional relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms and helps school mental health educators design targeted interventions to reduce problematic mobile phone use, sleep problems, and depressive symptoms among college students.