Enhancing specialization of attention-related EEG power and phase synchronism brain patterns by meditation.

Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain.

Abnormal Static and Dynamic Local Functional Connectivity in First-Episode Schizophrenia: A Resting-State fMRI Study.

Dynamic functional connectivity (FC) analyses have provided ample information on the disturbances of global functional brain organization in patients with schizophrenia. However, our understanding about the dynamics of local FC in never-treated first episode schizophrenia (FES) patients is still rudimentary. Dynamic Regional Phase Synchrony (DRePS), a newly developed dynamic local FC analysis method that could quantify the instantaneous phase synchronization in local spatial scale, overcomes the limitations of commonly used sliding-window methods. The current study performed a comprehensive examination on both the static and dynamic local FC alterations in FES patients (N = 74) from healthy controls (HCs, N = 41) with resting-state functional magnetic resonance imaging using DRePS, and compared the static local FC metrics derived from DRePS with those calculated from two commonly used regional homogeneity (ReHo) analysis methods that are defined based on Kendall's coefficient of concordance (KCC-ReHo) and frequency coherence (Cohe-ReHo). Symptom severities of FES patients were assessed with a set of clinical scales. Cognitive functions of FES patients and HCs were assessed with the MATRICS consensus cognitive battery. Group-level analysis revealed that compared with HCs, FES patients exhibited increased static local FC in right superior, middle temporal gyri, hippocampus, parahippocampal gyrus, putamen, and bilateral caudate nucleus. Nonetheless, the dynamic local FC metrics did not show any significant differences between the two groups. The associations between all local FC metrics and clinical characteristics manifested scores were explored using a relevance vector machine. Results showed that the Global Assessment of Functioning score highest in past year and the Brief Visuospatial Memory Test-Revised task score were statistically significantly predicted by a combination of all static and dynamic features. The diagnostic abilities of different local FC metrics and their combinations were compared by the classification performance of linear support vector machine classifiers. Results showed that the inclusion of zero crossing ratio of DRePS, one of the dynamic local FC metrics, alongside static local FC metrics improved the classification accuracy compared to using static metrics alone. These results enrich our understanding of the neurocognitive mechanisms underlying schizophrenia, and demonstrate the potential of developing diagnostic biomarker for schizophrenia based on DRePS.

Association Between Metabolic Risk Factors and Cognitive Impairment in Schizophrenia Based on Sex.

OBJECTIVE: Sex differences have been observed in many aspects of schizophrenia, including cognitive deficits. Despite extensive research into the relationship between metabolic factors and cognitive deficits in schizophrenia, few studies have explored the potential sex difference in their association. METHODS: We recruited 358 schizophrenia patients and 231 healthy controls. The participants underwent measurements of body mass index (BMI), waist circumference, blood pressure, triglycerides, high-density lipoprotein cholesterol, and fasting blood glucose. Metabolic risk factors included abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. A collection of these metabolic risk factors has been defined as metabolic syndrome. These diagnoses were based on the criteria of the National Cholesterol Education Program's Adult Treatment Panel III. Cognitive performance was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A descriptive analysis, difference analysis, and linear regression model were used to identify the metabolic risk factors for cognitive function in schizophrenia. RESULTS: Our findings revealed sex differences in the rate of abdominal obesity and hypertension in schizophrenic patients. Additionally, we observed sex differences in the association between metabolic risk factors and cognitive impairment in schizophrenia. Specifically, hyperglycemia was associated with the immediate memory index score of RBANS in male patients, while dyslipidemia was associated with language, attention, delayed memory index scores, and RBANS total score in female patients. CONCLUSION: Our results suggest that sex should be considered when evaluating the impact of metabolic disorders on the cognitive function of schizophrenic patients. Moreover, our study identifies hyperglycemia and dyslipidemia as potential targets for precise treatment by sex stratification, which could benefit the improvement of cognitive impairment in schizophrenic patients.

Can rural e-commerce contribute to carbon reduction? A quasi-natural experiment based on China's e-commerce demonstration counties.

As information and communication technologies gain prominence in rural areas, rural e-commerce has emerged as a crucial mechanism for fostering agricultural and rural development. While the economic impact of rural e-commerce is widely studied, its effects on carbon emissions remain underexplored. Utilizing a multi-period DID model and Chinese county-level panel data from 2011 to 2017, this paper examines the impact of rural e-commerce on carbon emission reduction, employing the "comprehensive demonstration of e-commerce into villages" policy introduced by the Chinese government in 2014 as a quasi-natural experiment. Our findings show that e-commerce demonstration counties experienced a 10.6% reduction in carbon emissions compared to the control group, indicating a significant emission reduction effect. Further analysis reveals that rural e-commerce drives carbon reduction through decreased fertilizer usage, altered cropping structures, and enhanced transportation efficiency. Notably, rural e-commerce's emission reduction effect is more pronounced in major grain-producing regions and areas with higher digitalization levels. This study has crucial theoretical and practical implications for achieving carbon emission reduction and sequestration in agriculture and advancing green agricultural development.

Information technology and firm's green innovation: evidence from China.

Can the adoption of information technology (IT) by firms drive green innovation and stimulate sustainable development? This paper employs a quasi-natural experiment, namely the "Pilot Zone for Integration of Informatization and Industrialization" in China, to evaluate the effect of IT on green innovation. Our findings reveal that IT significantly fosters the proliferation of green invention patents but has inconsequential impact on green utility model patents. Notably, the positive effects of IT on green innovation are further amplified when firms receive scrutiny from analysts. Moreover, IT can facilitate green innovation in the source-governance category of conventional energy and not only in end-of-pipe regulatory compliance. The study generates valuable insights regarding the utilization of information technology by firms as a means to facilitate green innovation.

Evaluation of Brain Source Localization Methods Based on Test-Retest Reliability With Multiple Session EEG Data.

OBJECTIVE: Various EEG source localization methods have been proposed for functional brain research. The evaluation and comparison of these methods are usually based on simulated data but not real EEG data, as the ground truth of source localization is unknown. In this study, we aim to evaluate source localization methods quantitatively under the real situation. METHODS: We examined the test-retest reliability of the source signals reconstructed from a public six-session EEG data of 16 subjects performing face recognition tasks by five mainstream methods, including weighted minimum norm estimation (WMN), dynamical Statistical Parametric Mapping (dSPM), Standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA), dipole modeling and linearly constrained minimum variance (LCMV) beamformers. All methods were evaluated in terms of peak localization reliability and amplitude reliability of source signals. RESULTS: In the two brain regions responsible for static face recognition, all methods have promising peak localization reliability, with WMN showing the smallest peak dipole distance between session pairs. The spatial stability of source localization in the familiar face condition is better than those in the unfamiliar face and the scrambled face conditions in the face recognition areas in the right hemisphere. In addition, the test-retest reliability of source amplitude by all methods is good to excellent under the familiar face condition. CONCLUSION: Stable and reliable results for source localization can be obtained in the presence of evident EEG effects. Due to different levels of a priori knowledge, different source localization methods have different applicable scenarios. SIGNIFICANCE: These findings provide new evidence for the validity of source localization analysis and a new perspective for the evaluation of source localization methods on real EEG data.

Mindfulness-based intervention improves residual negative symptoms and cognitive impairment in schizophrenia: a randomized controlled follow-up study.

BACKGROUND: Residual negative symptoms and cognitive impairment are common for chronic schizophrenia patients. The aim of this study was to investigate the efficacy of a mindfulness-based intervention (MBI) on negative and cognitive symptoms of schizophrenia patients with residual negative symptoms. METHODS: In this 6-week, randomized, single-blind, controlled study, a total of 100 schizophrenia patients with residual negative symptoms were randomly assigned to the MBI or control group. The 6-week MBI group and the control group with general rehabilitation programs maintained their original antipsychotic treatments. The scores for the Positive and Negative Syndrome Scale (PANSS), the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and the Symptom Checklist 90 (SCL-90) were recorded at baseline and week 6 to assess psychotic symptoms, cognitive performance, and emotional state, respectively. RESULTS: Compared with general rehabilitation programs, MBI alleviated the PANSS-negative subscore, general psychopathology subscore, and PANSS total score in schizophrenia patients with residual negative symptoms (F = 33.77, pBonferroni < 0.001; F = 42.01, pBonferroni < 0.001; F = 52.41, pBonferroni < 0.001, respectively). Furthermore, MBI improved RBANS total score and immediate memory subscore (F = 8.80, pBonferroni = 0.024; F = 11.37, pBonferroni = 0.006), as well as SCL-90 total score in schizophrenia patients with residual negative symptoms (F = 18.39, pBonferroni < 0.001). CONCLUSIONS: Our results demonstrate that MBI helps schizophrenia patients with residual negative symptoms improve clinical symptoms including negative symptom, general psychopathology symptom, and cognitive impairment. TRIAL REGISTRATION: ChiCTR2100043803.

The impact of environmental information disclosure quality on green innovation of high-polluting enterprises.

With the gradual increase of social awareness of environmental protection, environmental information disclosure has become the key for enterprises to accept social supervision and fulfill their social responsibility. This study examines the high-polluting enterprises that were listed on Chinese A-shares between 2008 and 2021. The influence of environmental information disclosure quality on green innovation is examined using ordinary least squares (OLS) as a benchmark model. The results show that the improvement of environmental information disclosure quality of high-polluting enterprises can significantly improve the quantity and quality of green innovation of enterprises and are mediated by alleviating financing constraints and enhancing cash reserves. Moreover, improving the quality of environmental information disclosure of highly polluting enterprises has a more significant contribution to the quantity and quality of green patents of non-state-owned enterprises, enterprises located in central and eastern China, and large enterprises. The findings of this paper provide theoretical support for achieving a "win-win" situation of environmental protection and green innovation.