The brain activation of anxiety disorders with emotional stimuli-an fMRI ALE meta-analysis.

Numerous studies have analyzed the state of brain activation about anxiety disorders under emotional stimuli. However, there is no meta-analysis to assess the commonality and specificity activation concerning different subtypes of anxiety. Here, we used ALE to assess this. 29 studies revealed increased bilateral amygdala, anterior cingulate gyrus, parahippocampal gyrus activation in anxiety disorders during emotional stimuli. Moreover, we observed decreased activations in the posterior cingulate, lingual gyrus, and precuneus. In sub-analysis, although different anxiety showed dissimilar activations, the principal activations were observed in limbic lobe, which might indicate the limbic circuit was the main neural reflection of anxiety symptoms.

Dynamic brain entropy predicts risky decision-making across transdiagnostic dimensions of psychopathology.

OBJECTIVES: Maladaptive risky decision-making is a common pathological behavior among patients with various psychiatric disorders. Brain entropy, which measures the complexity of brain time series signals, provides a novel approach to assessing brain health. Despite its potential, the dynamics of brain entropy have seldom been explored. This study aimed to construct a dynamic model of brain entropy and examine its predictive value for risky decision-making in patients with mental disorders, utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: This study analyzed the rs-fMRI data from a total of 198 subjects, including 48 patients with bipolar disorder (BD), 47 patients with schizophrenia (SZ), 40 patients with adult attention deficit hyperactivity disorder (ADHD), as well as 63 healthy controls (HC). Time series signals were extracted from 264 brain regions based on rs-fMRI. The traditional static entropy and dynamic entropy (coefficient of variation, CV; rate of change, Rate) were constructed, respectively. Support vector regression was employed to predict risky decision-making utilizing leave-one-out cross-validation within each group. RESULTS: Our findings showed that CV achieved the best performances in HC and BD groups (r = -0.58, MAE = 6.43, R2 = 0.32; r = -0.78, MAE = 12.10, R2 = 0.61), while the Rate achieved the best in SZ and ADHD groups (r = -0.69, MAE = 10.20, R2 = 0.47; r = -0.78, MAE = 7.63, R2 = 0.60). For the dynamic entropy, the feature selection threshold rather than the time window length and overlapping ratio influenced predictive performance. CONCLUSIONS: These results suggest that dynamic brain entropy could be a more effective predictor of risky decision-making than traditional static brain entropy. Our findings offer a novel perspective on exploring brain signal complexity and can serve as a reference for interventions targeting risky decision-making behaviors, particularly in individuals with psychiatric diagnoses.

Editorial for the Special Issue on Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems.

Smart sensing and advanced systems have played crucial roles in the modern industrialization of society, which has led to many sensors being used in fabrication methodologies for various applications, such as in medical equipment [...].

Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects.

Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data-driven artificial intelligence systems. This review provides a unique perspective on recent progress in data-driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high-throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics-informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics-informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data-driven characterization and prognosis are discussed toward accelerating energy device development with much-enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next-generation battery development.

Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy.

Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in "cold" plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare.

Empirical evidence for the neurocognitive effect of nitrous oxide as an adjunctive therapy in patients with treatment resistant depression: A randomized controlled study.

Nitrous oxide (N2O) has demonstrated an antidepressant effect for treatment-resistant depression (TRD), but no studies investigated the effects of N2O on different cognition domains. This study aimed to test whether N2O would display pro-cognitive effects. We conducted a double-blinded, placebo-controlled, randomized controlled trial, 44 patients with TRD were randomized to N2O group (one-hour inhalation of 50% N2O/50% oxygen) or placebo group (50% air/50% oxygen). Thirty-four patients completed cognitive tests at the pre-treatment phase, 1-week, and 2 weeks post-treatment including subjective cognitive function, processing speed, attention, and executive function. Although the antidepressant effect of N2O was not significant at 1 week, patients still showed better performance of executive function at 1 week after receiving N2O compared with the placebo. Moreover, this significant improvement still existed at 1 week after controlling for the change in depressive symptoms over-time. Additionally, no significant difference was observed in subjective cognitive function, processing speed, and attention between these two groups across the 2-week follow-up period. As the first study investigating the treatment effects of N2O on improving cognitive function in TRD patients, the current study indicated that N2O has a potential pro-cognitive effect on executive function and this effect might be independent from improvements in depressive symptoms.

Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study.

Objectives: Several studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated. Methods: Nineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored. Results: ECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes. Conclusion: ECT modulated the brain's global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission. Significance: These findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing.

Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an artificial intelligence model to solve these two problems simultaneously. Focusing on the two problems, a novel auxiliary regression using a self-attention-augmented generative adversarial network (AR-SAGAN) is proposed in this paper. This model deals with the problem of NCM process modeling with three steps. First, the AR-SAGAN structure was established and composed of a generator, feature extractor, discriminator, and regressor. Second, the nanoscale coating quality was estimated by putting online control parameters into the feature extractor and regressor. Third, the control parameters in the recipes were generated using preset parameters and target quality. Finally, the proposed method was verified by the experiments of a solar cell antireflection coating dataset, the results of which showed that our method performs excellently for both multivariable quality prediction and data augmentation. The mean squared error of the predicted thickness was about 1.6~2.1 nm, which is lower than other traditional methods.

Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis.

Entropy is a measurement of brain signal complexity. Studies have found increased/decreased entropy of brain signals in psychiatric patients. There is no consistent conclusion regarding the relationship between the entropy of brain signals and mental illness. Therefore, this meta-analysis aimed to identify consistent abnormalities in the brain signal entropy in patients with different mental illnesses. We conducted a systematic search to collect resting-state functional magnetic resonance imaging (rs-fMRI) studies in patients with psychiatric disorders. This work identified 9 eligible rs-fMRI studies, which included a total of 14 experiments, 67 activation foci, and 1383 subjects. We tested the convergence across their findings by using the activation likelihood estimation method. P-value maps were corrected by using cluster-level family-wise error p < 0.05 and permuting 2000 times. Results showed that patients with different psychiatric disorders shared commonly increased entropy of brain signals in the left inferior and middle frontal gyri, and the right fusiform gyrus, cuneus, precuneus. No shared alterations were found in the subcortical regions and cerebellum in the patient group. Our findings suggested that the increased entropy of brain signals in the cortex, not subcortical regions and cerebellum, might have associations with the pathophysiology across mental illnesses. This meta-analysis study provided the first comprehensive understanding of the abnormality in brain signal complexity across patients with different psychiatric disorders.

Efficacy and safety of nitrous oxide for patients with treatment-resistant depression, a randomized controlled trial.

Nitrous oxide (N2O), an N-methyl-D-aspartate glutamate receptor antagonist, has demonstrated a rapid-onset antidepressant effect for treatment-resistant depression (TRD) preliminarily in the United States. This study aimed to test the efficacy and safety of N2O for TRD patients in China. In this double-blinded, placebo-controlled trial, 44 patients with TRD were randomized to receive a one-hour inhalation of a mixture of either 50% N2O/50% oxygen (N2O group) or 50% air/50% oxygen (placebo group). The primary outcome was the change on the 17-item Hamilton Depression Rating Scale (HDRS-17) over a course of two weeks. Using modified intention-to-treat analysis, the between group difference was found in the HDRS-17 score at two hours and 24 h after inhalation treatment, while no significant difference was found in week one and week two. Patients receiving N2O reported a significantly higher number of adverse events. All the adverse events lasted for no more than 24 h. No serious adverse events were reported. A single inhalation of 50% N2O effectively alleviates depression in patients with TRD in China. The efficacy lasts for no more than one week. N2O is safe for patients with TRD.

Trait and state alterations in excitatory connectivity between subgenual anterior cingulate cortex and cerebellum in patients with current and remitted depression.

Neuroimaging studies have indicated that the altered functional connectivity (FC) of the subgenual anterior cingulate cortex (sgACC) might be potential pathophysiology of major depressive disorder (MDD). However, directed connectivity is proven to be more closely to neurophysiological processes underlying brain activity than FC. This study aimed to identify the alterations underlying directed connectivity of the sgACC in patients with current and remitted MDD. We conducted a cross-sectional neuroimaging study by recruiting 36 patients with current MDD, 20 patients with remitted MDD, and 36 matched healthy controls. Multiple linear regression was employed to estimate bidirectional connectivity between bilateral sgACC and 115 brain regions over 230 time points. Besides, graph theory was applied to further investigate the information transfer across bilateral sgACC and abnormal brain regions. We found that both patients with current and remitted MDD showed a similar abnormality in bidirectional excitatory connectivity between the left sgACC and the right cerebellum. Patients with current MDD exhibited an increase in excitatory connectivity from the left cerebellum to the right sgACC, which was positively correlated with the HAMD score. Meanwhile, significantly decreased betweenness of the left sgACC was detected in all depressive patients. Our findings suggest that the changed bidirectional excitatory connectivity between the left sgACC and the right cerebellum might be a trait alteration and the abnormal increased excitatory connectivity from the left cerebellum to the right sgACC might be a state alteration of MDD. This work may provide a valuable contribution to identify trait and state alterations in the brain for depression.

Aberrant Temporal Variability in Brain Regions during Risk Decision Making in Patients with Bipolar I Disorder: A Dynamic Effective Connectivity Study.

Bipolar I disorder (BD-I) is associated with high-risk behaviors, such as suicide attempts and addictive substance abuse. Understanding brain activity exposure to risk decision making provides evidence for the treatment of BD-I patients. This study aimed to investigate the temporal dynamics of brain connectivity underlying risk decision making in patients with BD-I. A total of 101 subjects (48 BD-I patients and 53 age- and gender-matched healthy controls (HCs)) were included in this research. We analyzed the fMRI data acquired during Balloon Analog Risk Task (BART) performance. Voxel-wise dynamic effective connectivity (dEC) was employed to measure the activities in 264 brain regions. The coefficient of variation (CV) was calculated as temporal dynamics of brain connectivity. Finally, we used structural equation modeling (SEM) to determine the relationships of dEC in brain regions with clinical symptoms, behavior performances in patients. Results showed that BD-I patients exhibited increased dynamics in four lobes and exhibited decreased in three frontal regions. Besides, SEM results showed that the impulsive symptoms of patients were affected by the dEC during both resting and task states. Moreover, the dEC of left supramarginal gyrus (SMG) influenced those of left orbital frontal and right cuneus (CUN), as well as the affective symptoms and BART behaviors in patients with BD-I. Our results suggested that the altered temporal dynamics of brain connectivity might contribute to the impulsivity of BD-I during resting and task states. More importantly, the left SMG might be a therapeutic target to reduce the risk behavior in BD-I patients.

Decreased dynamism of overlapping brain sub-networks in Major Depressive Disorder.

Major Depressive Disorder (MDD) is increasingly recognized as a common brain disorder with aberrant brain networks. Alterations in dynamic functional brain networks have been widely reported in MDD. However, previous studies mainly focused on detecting non-overlapping sub-networks/communities, neglecting the possibility that one brain region may belong to multiple sub-networks/communities. In the present work, we utilized tensor decomposition method to detect overlapping communities and study the dynamism of overlapping sub-networks through 58 patients with MDD and 63 age- and sex-matched healthy controls (HC). The strength vectors of communities were calculated and two-sample t-test was performed to investigate the statistical significance of the differences in dynamism of MDD and HC groups. We found that communities detected in two groups were pairwise region-matching but overlapped brain regions were almost totally different. We considered two region-matching communities in the two groups as a sub-network. Compared to HCs, MDD patients showed significantly decreased dynamism in five sub-networks which could be functionally mapped to Visual Network (VN), Default Mode Network (DMN), Cognitive Control Network (CCN), Bilateral Limbic Network (BLN) and Auditory Network (AN). The results showed that MDD might only have a marginal effect on the holistic detection of communities and the changes of overlapped brain regions in MDD patients might be put down to the alteration of hubs. Further statistical analysis on nine sub-networks showed decreased dynamism of five sub-networks in MDD patients, which might help us achieve a better understanding of mechanism in MDD.

A study of the relationship between adverse childhood experiences, life events, and executive function among college students in China.

OBJECTIVE: The aim of this study was to investigate the effects of adverse childhood experiences and life events on the inhibitory control ability, cognitive flexibility, and working memory of college students. METHODS: The study involved testing the participants using the Adverse Childhood Experiences (ACEs) Questionnaire, the Adolescent Life Events Scale (Adolescent Self-rating Life Events Checklist, ASLEC), and the program of executive functions designed by E-prime software. RESULTS: The incidence rate of ACEs was 44.8%. ACEs, life events, and inhibition ability were found to have a significant correlation (r1 = 0.50, r1 = 0.47, p < 0.01). In the switching task, the reaction time of the ACEs group was longer than the reaction time of the non-ACEs group (t = - 2.55, p < 0.05). Low scorers in the ASLEC exhibited lesser reaction times than their high-scoring counterparts in the tasks related to inhibition, switching, and working memory experiments. The regression analysis results showed that ACEs and life events had a possibility rate of 56% in predicting inhibition ability. CONCLUSIONS: The incidence of ACEs was found to be high, and cognitive flexibility is significantly influenced by ACEs. Life events have a significant impact on inhibition ability, cognitive flexibility, and working memory. ACEs and life events were found to be reliable predictors of inhibition ability.

A study of the relationship between adverse childhood experiences, life events, and executive function among college students in China

Abstract Objective: The aim of this study was to investigate the effects of adverse childhood experiences and life events on the inhibitory control ability, cognitive flexibility, and working memory of college students. Methods: The study involved testing the participants using the Adverse Childhood Experiences (ACEs) Questionnaire, the Adolescent Life Events Scale (Adolescent Self-rating Life Events Checklist, ASLEC), and the program of executive functions designed by E-prime software. Results: The incidence rate of ACEs was 44.8%. ACEs, life events, and inhibition ability were found to have a significant correlation (r1 = 0.50, r1 = 0.47, p < 0.01). In the switching task, the reaction time of the ACEs group was longer than the reaction time of the non-ACEs group (t = ­ 2.55, p < 0.05). Low scorers in the ASLEC exhibited lesser reaction times than their high-scoring counterparts in the tasks related to inhibition, switching, and working memory experiments. The regression analysis results showed that ACEs and life events had a possibility rate of 56% in predicting inhibition ability. Conclusions: The incidence of ACEs was found to be high, and cognitive flexibility is significantly influenced by ACEs. Life events have a significant impact on inhibition ability, cognitive flexibility, and working memory. ACEs and life events were found to be reliable predictors of inhibition ability.