Lower Small-Worldness of Intrinsic Brain Networks Facilitates the Cognitive Protection of Intellectual Engagement in Elderly People Without Dementia: A Near-Infrared Spectroscopy Study.

OBJECTIVE: Lifetime intellectual engagement may be associated with cognitive ability late in life. However, the current evidence on whether cognitive activities will improve and/or maintain cognitive function is heterogeneous. Drawing on knowledge of the brain's intrinsic small-world organization which combines regional specialization and efficient global information transfer, we aimed to explore that whether individual differences in the small-worldness of resting-state functional connectivity (rsFC) networks would explain the variability in the strength of the association between intellectual engagement and cognitive functioning. METHODS: Sixty-five elderly people without dementia were enrolled and scanned with a 52-channel near-infrared spectroscopy system. The number, frequency, and participation hours of intellectual activities were investigated to measure intellectual engagement. Global cognition was assessed by the Montreal Cognitive Assessment. The general linear models and the simple slope analysis were employed to measure the modulatory role of network properties. RESULTS: The small-worldness of the brain network emerged as a moderator of the association between intellectual engagement and cognition. Exclusively among elderly people with lower small-worldness, greater intellectual engagement, including the frequency and participation hours of activities, was associated with greater global cognitive function. Furthermore, we observed that elderly people with lower small-worldness exhibited decreased rsFC across the bilateral frontopolar areas and increased rsFC across the bilateral parietal cortex. CONCLUSION: The individual differences in the small-worldness of rsFC networks might explain the varying strength of the association between intellectual engagement and cognitive functioning. Our findings imply that the intrinsic small-worldness of the brain network might be a potential neurobiological contributor that interacts with the intellectual engagement in enhancing the cognitive ability in late life.

Association Between Alexithymia and Immature Coping Styles Is Mediated by Self-Inconsistency and Is Correlated to Obsessive-Compulsive Symptoms.

This study aimed to investigate whether a multivariate association between alexithymia and coping styles is affected by self-inconsistency and whether the association contributed to obsessive-compulsive symptoms in 34 patients with obsessive-compulsive disorder (OCD) and 53 healthy participants. Alexithymia, coping styles, self-inconsistency, and obsessive-compulsive symptoms were evaluated using the Toronto Alexithymia Scale-20, the Coping Style Questionnaire, the Self-Consistency and Congruence Scale, and the Vancouver Obsessive-Compulsive Inventory, respectively. We found that self-inconsistency partially mediated the association between alexithymia and immature coping styles in the OCD patients but fully mediated the association in the healthy participants. Moreover, in the two groups, the alexithymia-coping coupling was related to contamination, obsession, indecisiveness, and ritualization, but not the checking symptom dimension. The findings suggest that the disturbance in identifying and describing feelings is associated with immature coping styles through disharmony between self-recognition and experience. The mechanism of the checking symptom seems to be distinct from other obsessive-compulsive symptoms.

Antipsychotic polypharmacy in the treatment of schizophrenia in China and Japan.

BACKGROUND: Although antipsychotic monotherapy is recommended as the main treatment for schizophrenia, antipsychotic polypharmacy is not rare in practice. However, longitudinal data on antipsychotic polypharmacy in schizophrenia treatment are limited. METHODS: This longitudinal database study described antipsychotic polypharmacy in the treatment of schizophrenia in real-world settings in China and Japan. We retrieved information about antipsychotic treatment for schizophrenia from January 2010 to December 2014 from two hospital Electronic Medical Records databases in China and one claims database, Japan Medical Data Centre in Japan. Eligible patients had a diagnosis of schizophrenia (International Classification of Diseases, Tenth Revision F20.x) and at least one prescription for first or second generation antipsychotics. Antipsychotic polypharmacy was defined as having more than one antipsychotic medication overlapping for ⩾60 days. The Japan Medical Data Centre study cohort was further stratified by employees (insurance beneficiaries) and their dependents. RESULTS: The study cohorts comprised 11,961 patients from China and 25,034 (10,661 employee sub-cohort and 14,373 dependent sub-cohort) from 14 days Japan Medical Data Centre in Japan. Most patients were prescribed monotherapy (87.3% in China and 80.1% in Japan), of which oral second-generation antipsychotics were the majority (78.9% in China and 65.8% in Japan). The prevalence rate of antipsychotic polypharmacy was 12.7% in China and 19.9% in Japan (13.7% in employees vs 24.5% in dependents). The most common combinations were two oral antipsychotics. Combinations of more than two drugs were uncommon in China (0.3%) but were prescribed for 5.3% of patients in Japan. Among patients treated with monotherapy, 12.6/100 person-years (11.8%) in China and 9.6/100 person-years (11.0%) in Japan switched to antipsychotic polypharmacy during follow-up. Younger patients were more likely to switch to antipsychotic polypharmacy than older patents in all study cohorts. CONCLUSION: The observed rates of antipsychotic polypharmacy ranged from 12.7% in China to 19.9% in Japan. Switching from monotherapy to antipsychotic polypharmacy was most likely to occur in younger patients with schizophrenia.

Alexithymia, Ego-Dystonicity, and Obsessive-Compulsive Symptoms: A Path Modeling Analysis.

AIMS: This cross-sectional study aimed to test the path relations between alexithymia, ego-dystonicity, anxiety, depression and obsessive-compulsive (OC) symptoms in obsessive-compulsive disorder (OCD) and healthy individuals. METHODS: Fifty-eight patients with OCD (mean age 35.5 years) and 54 healthy participants (mean age 33.5 years) completed an assessment via a structured clinical interview. All of them completed the Toronto Alexithymia Scale (TAS-20), the Vancouver Obsessive-Compulsive Inventory (VOCI), the Self-Consistency and Congruence Scale (SCCS), the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS). The data were analyzed using partial least squares structural equation modeling (PLS-SEM). RESULTS: In the OCD patients, alexithymia (a linear combination of difficulty identifying and describing emotions in the self) was associated with the OC symptoms either with or without the presence of ego-dystonicity (a profile of self-inconsistency and self-stereotype). In the heathy participants, alexithymia was associated with the OC symptoms only through ego-dystonic experiences. CONCLUSION: This study provides evidence that ego-dystonicity partially affects the association between alexithymia and obsessive-compulsions. Alexithymia and ego-dystonicity have a synergistic effect on the symptoms of OCD. Alexithymia in healthy participants associates to the OC symptoms only through ego-dystonicity. Targeting ego-dystonicity dimensions in psychotherapy would help improve the symptoms of OCD.

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

BACKGROUND: Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. METHODS: Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. RESULTS: The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. CONCLUSIONS: Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

Near-infrared spectroscopy for examination of prefrontal activation during cognitive tasks in patients with major depressive disorder: a meta-analysis of observational studies.

AIMS: Near-infrared spectroscopy has the potential for aiding the diagnosis of major depressive disorder. The purpose of this study was to systematically review the evidence from observational studies regarding the use of near-infrared spectroscopy in patients with major depressive disorder and to identify the characteristic pattern of prefrontal lobe activity in major depressive disorder. METHODS: medline, PubMed, Cochrane Library and Web of Science databases were searched in December 2013. All case-control studies were included. The quality of evidence was examined using the Newcastle-Ottawa Quality Assessment Scale. The primary outcome measures were the mean oxygenated and deoxygenated hemoglobin alterations of the cerebral cortex during cognitive activation periods. The standard mean difference for the overall pooled effects across the included studies was estimated using random or fixed effect models. The primary outcome measures were included in the meta-analysis. RESULTS: Fourteen studies met the inclusion criteria. Six studies (n = 692 participants) were included in the analysis of the mean oxygenated hemoglobin alterations; the pooled mean standardized difference was -0.74 (95% confidence interval, -0.97 to -0.52), indicating that patients with major depressive disorder were associated with attenuated increase in oxygenated hemoglobin during cognitive activation in the prefrontal regions compared to healthy controls. Five studies (n = 668 participants) were included in the analysis of mean deoxygenated-hemoglobin changes; the pooled standardized mean difference was 0.18 (95% confidence interval, -0.20 to 0.56). CONCLUSIONS: Using near-infrared spectroscopy measurements, we observed that compared to healthy subjects, patients with major depressive disorder had significantly lower prefrontal activation during cognitive tasks.

Depressive and mania mood state detection through voice as a biomarker using machine learning.

Introduction: Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA). Results: Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%. Discussion: These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.

Abnormal trial-to-trial variability in P300 time-varying directed eeg network of schizophrenia.

Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300 has been proven to serve as a reliable psychosis endophenotype. The instability of neural processing across trials, i.e., trial-to-trial variability (TTV), is getting increasing attention in uncovering how the SCH "noisy" brain organizes during cognition processes. Nevertheless, the TTV in the brain network remains unrevealed, notably how it varies in different task stages. In this study, resorting to the time-varying directed electroencephalogram (EEG) network, we investigated the time-resolved TTV of the functional organizations subserving the evoking of P300. Results revealed anomalous TTV in time-varying networks across the delta, theta, alpha, beta1, and beta2 bands of SCH. The TTV of cross-band time-varying network properties can efficiently recognize SCH (accuracy: 83.39%, sensitivity: 89.22%, and specificity: 74.55%) and evaluate the psychiatric symptoms (i.e., Hamilton's depression scale-24, r = 0.430, p = 0.022, RMSE = 4.891; Hamilton's anxiety scale-14, r = 0.377, p = 0.048, RMSE = 4.575). Our study brings new insights into probing the time-resolved functional organization of the brain, and TTV in time-varying networks may provide a powerful tool for mining the substrates accounting for SCH and diagnostic evaluation of SCH.

The moderating role of psychological resilience in the relationship between falls, anxiety and depressive symptoms.

BACKGROUND: There is a greater likelihood of anxiety and depression among older adults who suffer falls. This study examined the relationships of falls and severe falls with anxiety and depressive symptoms, and the moderating role of psychological resilience on these associations. METHODS: Our study recruited participants from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), a nationally representative cohort study. A total of 11,857 participants included in the analysis. We used a linear regression model to investigate the relationship between falls/severe falls and anxiety/depressive symptoms, adjusting for a range of potential covariates and a bootstrapping sample test to examine the potential moderating role of psychological resilience in these relationships. RESULTS: Older adults who suffered the falls have higher anxiety/depressive symptoms (ß = 0.28 [0.23, 0.32] for anxiety symptoms, p < 0.001; ß = 0.21 [0.16, 0.25] for depressive symptoms, p < 0.001), and those who suffered the severe falls have higher anxiety/depressive symptoms (ß = 0.30 [0.24, 0.37] for anxiety symptoms, p < 0.001; ß = 0.21 [0.15, 0.27] for depressive symptoms, p < 0.001), in the fully adjusted model. The relationship between falls/severe falls and anxiety/depressive symptoms was mitigated in participants with higher levels of psychological resilience. LIMITATIONS: The present study is based on cross-sectional data, which limits the ability to infer causal relationships. CONCLUSIONS: Falls/severe falls were positively associated with anxiety and depression, and that psychological resilience could moderate this association. Our findings suggest that psychological resilience may be an effective target for intervention and prevention of fall-related symptoms of anxiety and depression.

Discrimination of auditory verbal hallucination in schizophrenia based on EEG brain networks.

Auditory verbal hallucinations (AVH) are a core positive symptom of schizophrenia and are regarded as a consequence of the functional breakdown in the related sensory process. Yet, the potential mechanism of AVH is still lacking. In the present study, we explored the difference between AVHs (n = 23) and non-AVHs (n = 19) in schizophrenia and healthy controls (n = 29) by using multidimensional electroencephalograms data during an auditory oddball task. Compared to healthy controls, both AVH and non-AVH groups showed reduced P300 amplitudes. Additionally, the results from brain networks analysis revealed that AVH patients showed reduced left frontal to posterior parietal/temporal connectivity compared to non-AVH patients. Moreover, using the fused network properties of both delta and theta bands as features for in-depth learning made it possible to identify the AVH from non-AVH patients at an accuracy of 80.95%. The left frontal-parietal/temporal networks seen in the auditory oddball paradigm might be underlying biomarkers of AVH in schizophrenia. This study demonstrated for the first time the functional breakdown of the auditory processing pathway in the AVH patients, leading to a better understanding of the atypical brain network of the AVH patients.

Similarities and Differences in Brain Activation Between Patients With Schizophrenia and Obsessive-Compulsive Disorder: A Near-Infrared Spectroscopy Study.

Schizophrenia (SZ) and obsessive-compulsive disorder (OCD) share several epidemiological and clinical features, but the neurobiological substrates shared by these two diseases remain unclear. This study aimed to explore the similarities and differences in brain function between them using near-infrared spectroscopy (NIRS). Eventually, 130 SZ patients, 70 OCD and 75 normal controls (NCs) were enrolled. A 52-channel NIRS instrument was used to detect the concentration changes in oxygenated hemoglobin ([oxy-Hb]) during the verbal fluency task. Ten regions of interests (ROIs) were defined: the bilateral dorsolateral prefrontal cortex (DLPFC), frontopolar cortex (FPC), orbitofrontal cortex (OFC), inferior prefrontal gyrus (IFG) and temporal gyrus (TG). Through two different analysis strategies based on channels or ROIs, we compared the [oxy-Hb] changes in three groups by one-way analysis of variance (ANOVA) and post-hoc tests. Across 52 channels, compared to the NC group, both SZ and OCD groups exhibited reduced activity in 17 channels, including left FPC, left DLPFC, bilateral OFC, IFG, middle TG, supplementary motor cortex and Broca's area, while SZ showed lower activity in channel 35 (right OFC) than OCD patients. Across all ROIs, compared to the NC group, both SZ and OCD groups showed reduced activity in 7 ROIs, including left FPC, bilateral OFC, IFG and TG, while SZ showed lower activity in the right OFC than OCD group, which were almost consistent with the results based on channels. This study suggests SZ and OCD present with some similar neuropathological changes, while SZ shows more severe impairment in the right OFC than OCD.

Altered temporal variability in brain functional connectivity identified by fuzzy entropy underlines schizophrenia deficits.

Investigation of the temporal variability of resting-state brain networks informs our understanding of how neural connectivity aggregates and disassociates over time, further shedding light on the aberrant neural interactions that underlie symptomatology and psychosis development. In the current work, an electroencephalogram-based sliding window analysis was utilized for the first time to measure the nonlinear complexity of dynamic resting-state brain networks of schizophrenia (SZ) patients by applying fuzzy entropy. The results of this study demonstrated the attenuated temporal variability among multiple electrodes that were distributed in the frontal and right parietal lobes for SZ patients when compared with healthy controls (HCs). Meanwhile, a concomitant strengthening of the posterior and peripheral flexible connections that may be attributed to the excessive alertness or sensitivity of SZ patients to the external environment was also revealed. These temporal fluctuation distortions combined reflect an abnormality in the coordination of functional network switching in SZ, which is further the source of worse task performance (i.e., P300 amplitude) and the negative relationship between individual complexity metrics and P300 amplitude. Notably, when using the network metrics as features, multiple linear regressions of P300 amplitudes were also exactly achieved for both the SZ and HC groups. These findings shed light on the pathophysiological mechanisms of SZ from a temporal variability perspective and provide potential biomarkers for quantifying SZ's progressive neurophysiological deterioration.

One-year incidence rate of Treatment Resistant Depression (TRD) and treatment characteristics in China.

BACKGROUND: Little is known about the characteristics of Treatment-Resistant Depression (TRD) in China. In previous studies various identification approaches have led to a wide range of results, and it is unclear how Chinese patients compare to those in other studies. METHODS: This is a retrospective cohort study using electronic health records (EHR) from two major psychiatric hospitals in China. Adult major depressive disorder (MDD) patients who initiated pharmaceutical treatment during 2010-2018 were enrolled and follow-up was 1 year. TRD was primarily identified by consensus definition of two failures of adequate (≥4 weeks) regimens. Alternative regimens of 2-weeks and 6-weeks duration, and a data-driven definition were also applied. RESULTS: In the two hospitals, 12,257 (mean age: 40.8y, 63.6% female) and 8314 (mean age: 42.4y, 68.4% female) eligible patients were included. The 1-year incidence rate of TRD was estimated to be 5.2%-7.7% using the primary definition. TRD patients had mean treatment duration of 302.5 days and 285.7 days; had 3.6 and 3.7 treatment steps on average; 94.0% and 72.6% were prescribed polypharmacy regimens, which were all marginally greater than that of non-TRD patients. Alternative definitions resulted in a wide range of incidence estimates (0.5%-20.0%). LIMITATIONS: Medications were assumed to be consumed as prescribed and lack of rating scales from EHRs may limit our TRD identification. CONCLUSIONS: The incidence of TRD among Chinese MDD patients was comparable to other countries under similar settings and more complex treatment characteristics were observed among TRD patients. Alternative TRD definitions revealed the need for better treatment management in practices.

Prevalence and correlates of depression and anxiety among Chinese international students in US colleges during the COVID-19 pandemic: A cross-sectional study.

BACKGROUND: Previous studies showed that the COVID-19 outbreak increased the levels of depression and anxiety in heterogeneous populations. However, none has explored the prevalence and correlates of depression and anxiety among Chinese international students studying in US colleges during the pandemic. OBJECTIVE: This study examines the prevalence of depression and anxiety among Chinese international students enrolled in US universities during the COVID-19 pandemic and identifies the associated factors, including habits, social and psychological support, sleep quality, and remote learning. METHODS: Between June and July 2020, we conducted a cross-sectional study through Wenjuanxing, a web-based survey platform. Participants were recruited with snowball sampling through 21 Chinese international student associations in US universities. The survey consisted of demographic questions, the Social Support Rating Scale (SSRS), the Insomnia Severity Index (ISI), the Patient Health Questionnaire-9 (PHQ-9), the General Anxiety Disorder-7 (GAD-7), and self-constructed questions on academic performance, financial concerns, use of social media, physical exercise, and psychological support. Cut-off scores of 10 were used for both PHQ-9 and GAD-7 to determine the binary outcomes of depression and anxiety, respectively. Bivariant analyses and multivariable logistic regression analyses were performed to identify the associated factors. RESULTS: Among 1881 participants, we found a prevalence of depression (PHQ-9 score⩾ 10) at 24.5% and that of anxiety (GAD-7 score⩾ 10) at 20.7%. A higher risk of depression was associated with recent exposure to traumatic events, agreement to pandemic's negative impacts on financial status, agreement and strong agreement to the negative impacts of remote learning on personal relationships, and a higher ISI score. A lower risk of depression was associated with disagreement to the negative impacts of remote learning on academic performance and future careers, strong willingness to seek professional help with emotional issues, and a higher SSRS score. In addition, a higher risk of anxiety was associated with recent exposure to traumatic events, a lot of workloads, often staying up for online classes, agreement and strong agreement to the negative impacts of remote learning on personal relationships, and a higher ISI score. A lower risk of anxiety was associated with the willingness and strong willingness to seek professional help with emotional issues, and a higher SSRS score. CONCLUSION: This study showed a high prevalence of depression and anxiety among Chinese international students studying in US colleges during the COVID-19 pandemic. Multiple correlates-including recent exposure to traumatic events, pandemic-related financial concerns, workload, social support, remote learning, willingness to seek professional help, and sleep quality-were identified. It is critical for future studies to further investigate this student population and for universities to provide more flexible learning options and more access to psychological services.

Obesity-Associated Anxiety Is Prevalent among College Students and Alleviated by Calorie Restriction.

Anxiety is a common disorder among college students, especially those with obesity. Obesity contributes to metabolic disorders and disturbs the neural functions, further leading to anxiety. In this cross-sectional study, we aimed to determine the association between obesity and anxiety among college students and identified the potential factors for obesity-associated anxiety. We evaluated the intervention effects of calorie restriction on anxiety. Self-reported questionnaires were distributed to 1381 college students from January to March in 2021. Anxiety was measured by the State-Trait Anxiety Inventory (STAI). Participants were classified into anxiety and non-anxiety groups according to their STAI scores. Chi-squared test and logistic regression were used to analyze the potential factors. We found that 383 college students exhibited anxiety, accounting for 30.1% among all included college students, which was higher than the global average. The association between anxiety and obesity was observed among college students (p = 0.009), especially in males (p = 0.007). We identified that pre-obesity (p = 0.012), unhealthy calorie intake (p = 0.001), dieting (p = 0.003) and high academic year (p = 0.006) as the risk factors for anxiety and found that the long sleep duration was a protective factor for anxiety (p < 0.001). We found that more obese students showed an improvement of anxiety than the underweight students after calorie restriction (p < 0.001). Collectively, our findings suggest that obesity-associated anxiety is prevalent among the college students and could be alleviated by moderate calorie restriction. It is necessary for students to receive anxiety management in their college life. Additionally, the proper calorie restriction should be promoted to help students protect against obesity and obesity-associated anxiety.

Persistence With and Adherence to Paliperidone Palmitate Once-Monthly Injection for Schizophrenia Treatment in China and Japan.

Objective: To describe persistence with and adherence to paliperidone palmitate once-monthly injection (PP1M) compared to oral second-generation antipsychotics (SGAs) in patients with schizophrenia in real-world settings in China and Japan.Methods: Patients with a schizophrenia diagnosis (ICD-10: F20.x) who received oral or injectable antipsychotics from study start (China: January 1, 2012; Japan: January 1, 2014) until December 31, 2017, were enrolled in this retrospective cohort study. The first PP1M or oral SGA prescription date during the study period was defined as the index date. Eligible patients were followed up for up to 1 year after the index date. Persistence was measured from the index date until discontinuation or reaching 1 year. Adherence was assessed by calculating the proportion of days covered (PDC). Multivariable regression models were used to adjust for potential confounders.Results: The study cohorts comprised 44,266 patients from Japan and 7,564 and 5,189 patients, respectively, from 2 hospitals in China. The PP1M group showed consistently lower risk of discontinuation; adjusted hazard ratios and 95% CIs were 0.75 (0.72-0.90) (Japan), and 0.76 (0.68-0.84) and 0.65 (0.56-0.76) (China) compared to oral SGAs. The PP1M group also showed better adherence; adjusted odds ratios and 95% CIs were 1.61 (1.22-2.11) (Japan), and 1.92 (1.53-2.41) and 2.25 (1.58-3.23) (China).Conclusions: Persistence and adherence were significantly higher in PP1M users than in oral SGAs users across 3 databases comprising patients in 2 countries in Asia.

The Effect of Blue-enriched White Light on Cognitive Performances and Sleepiness of Simulated Shift Workers: A Randomized Controlled Trial.

BACKGROUND: Shift work is associated with reduced performance and efficiency, the current study aimed at investigating whether blue-enriched white light could improve workers' performance. METHODS: The study, which adopted a randomized controlled trial, was conducted among 48 simulated shift workers. The participants performed sustained attention task, working memory task, and sleepiness task during night shift work. The data was analyzed using two-way repeated measure ANOVA. RESULTS: The results indicated that, compared to conventional light, participants' correct responses of the sustained attention significantly increased when they were exposed to blue-enriched white light, correspondingly, the commission errors and omission errors declined. Furthermore, the blue-enriched white light had a significant effect on the decrease of sleepiness. However, the working memory was not significantly affected. CONCLUSION: Exposing to blue-enriched white light can improve sustained attention and reduce sleepiness.

The effects of dynamic daylight-like light on the rhythm, cognition, and mood of irregular shift workers in closed environment.

Shift workers are mostly suffered from the disruption of circadian rhythm and health problems. In this study, we designed proper light environment to maintain stable circadian rhythm, cognitive performance, and mood status of shift workers. We used five-channel light-emitting diodes to build up the dynamic daylight-like light environment. The illuminance, correlated color temperature, and circadian action factor of light were tunable in the ranges of 226 to 678 lx, 2680 to 7314 K, and 0.32 to 0.96 throughout the day (5:30 to 19:40). During the nighttime, these parameters maintained about 200 lx, 2700 K, and 0.32, respectively. In this light environment, three subjects had engaged in shift work for 38 consecutive days. We measured plasma melatonin, activity counts, continuous performance tests, and visual analogue scale on mood to assess the rhythm, cognitive performance, and mood of subjects. After 38-day shift work, the subjects' peak melatonin concentration increased significantly. Their physiological and behavioral rhythms maintained stable. Their cognitive performance improved significantly after night work, compared with that before night work. Their mood status had no significant change during the 38-day shift work. These results indicated that the light environment was beneficial to maintain circadian rhythm, cognitive performance and mood status during long-term shift work in closed environment.

Classification of schizophrenia using general linear model and support vector machine via fNIRS.

Schizophrenia is a type of serious mental illness. In clinical practice, it is still a challenging problem to identify schizophrenia-related brain patterns due to the lack of objective physiological data support and a unified data analysis method, physicians can only use the subjective experience to distinguish schizophrenia patients and healthy people, which may easily lead to misdiagnosis. In this study, we designed an optimized data-preprocessing method accompanied with techniques of general linear model feature extraction, independent sample t-test feature selection and support vector machine to identify a set of robust fNIRS pattern features as a biomarker to discriminate schizophrenia patients and healthy people. Experimental results demonstrated that the proposed combination way of data preprocessing, feature extraction, feature selection and support vector machine classification can effectively identify schizophrenia patients and the healthy people with a leave-one-out-cross-validation classification accuracy of 89.5%.

Study of the SCL-90 Scale and Changes in the Chinese Norms.

Objective: This study aimed to investigate the Chinese norms for the Symptom Checklist 90 (SCL-90) scale and its application. Methods: In total, 7,489 adults from Tianjin and Qingdao in China were included. Their data were compared with the norm data of 1,388 people published by Jin et al., the combined norms published by Tang et al., the data of 2,808 adults published by Chen and Li, and the data of 1,890 adults from Tong in China. Results: In five different periods, notable changes were observed in each factor of the SCL-90 that significantly differed from the previous norms. The scores of each factor showed an increasing annual trend. Compulsion consistently obtained the highest scores, and phobia consistently obtained the lowest scores. The scores tended to decrease from compulsion to anxiety, and psychosis scored lower than paranoia. There was a significant difference in the detection rate between the critical screening value of two points and the standard score. Using the standard score as the critical value, the detection rate ranged between 13 and 16% and was relatively concentrated. Using two points as the critical value, the detection rate ranged between 38 and 50%. Conclusion: The usual model in China is not consistent with social development. Using two points as the critical value is no longer suitable for the SCL-90. New Chinese norms and measurement standards should be developed. The mean value plus one standard deviation could be used as the new measurement standard.