Long-Term Stress and Trait Anxiety Affect Brain Network Balance in Dynamic Cognitive Computations.

Long-term stress has a profound impact on executive functions. Trait anxiety is recognized as a vulnerable factor accounting for stress-induced adaptive or maladaptive effects. However, the neurocognitive mechanisms underlying long-term stress and trait anxiety interactions remain elusive. Here we investigated how long-term stress and trait anxiety interact to affect dynamic decisions during n-back task performance by altering functional brain network balance. In comparison to controls, participants under long-term stress experienced higher psychological distress and exhibited faster evidence accumulation but had a lower decision-threshold when performing n-back tasks in general. This corresponded with hyper-activation in the anterior insula, less deactivation in the default-mode network, and stronger default-mode network decoupling with the frontoparietal network. Critically, high trait anxiety under long-term stress led to slower evidence accumulation through higher frontoparietal activity during cognitively demanding task, and increased decoupling between the default-mode and frontoparietal networks. Our findings suggest a neurocognitive model of how long-term stress and trait anxiety interplay to affect latent dynamic computations in executive functioning with adaptive and maladaptive changes, and inform personalized assessments and preventions for stress vulnerability.

Inhibitory learning of phototaxis by honeybees in a passive-avoidance task.

Honeybees are a standard model for the study of appetitive learning and memory. Yet, fewer attempts have been performed to characterize aversive learning and memory in this insect and uncover its molecular underpinnings. Here, we took advantage of the positive phototactic behavior of bees kept away from the hive in a dark environment and established a passive-avoidance task in which they had to suppress positive phototaxis. Bees placed in a two-compartment box learned to inhibit spontaneous attraction to a compartment illuminated with blue light by associating and entering into that chamber with shock delivery. Inhibitory learning resulted in an avoidance memory that could be retrieved 24 h after training and that was specific to the punished blue light. The memory was mainly operant but involved a Pavlovian component linking the blue light and the shock. Coupling conditioning with transcriptional analyses in key areas of the brain showed that inhibitory learning of phototaxis leads to an up-regulation of the dopaminergic receptor gene Amdop1 in the calyces of the mushroom bodies, consistently with the role of dopamine signaling in different forms of aversive learning in insects. Our results thus introduce new perspectives for uncovering further cellular and molecular underpinnings of aversive learning and memory in bees. Overall, they represent an important step toward comparative learning studies between the appetitive and the aversive frameworks.

Altered brain activation and connectivity during anticipation of uncertain threat in trait anxiety.

In the research field of anxiety, previous studies generally focus on emotional responses following threat. A recent model of anxiety proposes that altered anticipation prior to uncertain threat is related with the development of anxiety. Behavioral findings have built the relationship between anxiety and distinct anticipatory processes including attention, estimation of threat, and emotional responses. However, few studies have characterized the brain organization underlying anticipation of uncertain threat and its role in anxiety. In the present study, we used an emotional anticipation paradigm with functional magnetic resonance imaging (fMRI) to examine the aforementioned topics by employing brain activation and general psychophysiological interactions (gPPI) analysis. In the activation analysis, we found that high trait anxious individuals showed significantly increased activation in the thalamus, middle temporal gyrus (MTG), and dorsomedial prefrontal cortex (dmPFC), as well as decreased activation in the precuneus, during anticipation of uncertain threat compared to the certain condition. In the gPPI analysis, the key regions including the amygdala, dmPFC, and precuneus showed altered connections with distributed brain areas including the ventromedial prefrontal cortex (vmPFC), dorsolateral prefrontal cortex (dlPFC), inferior parietal sulcus (IPS), insula, para-hippocampus gyrus (PHA), thalamus, and MTG involved in anticipation of uncertain threat in anxious individuals. Taken together, our findings indicate that during the anticipation of uncertain threat, anxious individuals showed altered activations and functional connectivity in widely distributed brain areas, which may be critical for abnormal perception, estimation, and emotion reactions during the anticipation of uncertain threat.

Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity.

Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole-brain resting-state functional connectivity (RSFC). Resting-state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole-brain RSFC and trait narcissism. We demonstrated that the machine-learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level.

Comparative transcriptome analysis of Apis mellifera antennae of workers performing different tasks.

Honey bee is a social insect. Its colony is mainly coordinated by the chemical signals such as pheromones produced by queen or brood. Correspondingly, the worker bee developed numerous complicated olfactory sensilla in antennae for detection of these colony chemical signals and nectar/pollen signals in foraging. With the normal development of new emerged workers, young adults (nurse bee) worked in colony at the first 2-3 weeks and then followed by the foraging activity outside of the hive, which give rise to great change of the surrounding chemical signals. However, the olfactory adaption mechanism of worker bee in these processes of behavioral development is still unclear. In this study, we conducted a comprehensive and quantitative analysis of gene expression in Apis mellifera antenna of newly emerged workers, nurses and foragers using transcriptome analysis. Meanwhile, we constructed experimental colonies to collect age-matched samples, which were used to determine whether task is the principal determinant of differential expression. RNA sequencing and quantitative real-time polymerase chain reaction revealed that 6 and 14 genes were closely associated with nurse and forager behaviors, respectively. Furthermore, a broad dynamic range of chemosensory gene families and candidate odorant degrading enzymes were analyzed at different behavior statuses. We firstly reported genes associated with nursing/foraging behavior from antennae and the variations of expression of genes belonging to various olfactory gene families at different development stages. These results not only could contribute to elucidating the relationship between olfactory and behavior-related changes, but also provide a new perspective into the molecular mechanism underlying honey bee division of labor.

Relationship between the LHPP Gene Polymorphism and Resting-State Brain Activity in Major Depressive Disorder.

A single-nucleotide polymorphism at the LHPP gene (rs35936514) has been reported in genome-wide association studies to be associated with major depressive disorder (MDD). However, the neural system effects of rs35936514 that mediate the association are unknown. The present work explores whether the LHPP rs35936514 polymorphism moderates brain regional activity in MDD. A total of 160 subjects were studied: a CC group homozygous for the C allele (23 individuals with MDD and 57 controls) and a T-carrier group carrying the high risk T allele (CT/TT genotypes; 22 MDD and 58 controls). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Brain activity was assessed using the amplitudes of low-frequency fluctuations (ALFF). MDD patients showed a significant increased ALFF in the left middle temporal gyrus and occipital cortex. The T-carrier group showed increased ALFF in the left superior temporal gyrus. Significant diagnosis × genotype interaction was noted in the bilateral lingual gyri, bilateral dorsal lateral prefrontal cortex (dlPFC), and left medial prefrontal cortex (mPFC) (P < 0.05, corrected). Results demonstrated that MDD patients with LHPP rs35936514 CT/TT genotype may influence the regional brain activity. These findings implicate the effects of the rs35936514 variation on the neural system in MDD.

CT perfusion-derived mean transit time of cortical brain has a negative correlation with the plasma level of Nitric Oxide after subarachnoid hemorrhage.

BACKGROUND: Vasospasm of both large and small parenchymal arteries may contribute to the occurrence of delayed ischemic neurological deficits, and nitric oxide(NO) is an important mediators in the development of cerebral vasospasm after subarachnoid hemorrhage (SAH). We used a rabbit two-hemorrhage model to investigate changes in plasma NO after SAH, and the relationship between NO and brain microcirculation. METHODS: SAH was induced in rabbits and a control group was sham operated. There were 32 rabbits in each group that survived the second operation, and they were randomly assigned to four groups of eight rabbits each for follow-up assessments on Days 1, 4, 7, or 14, respectively. Cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) were calculated at six regions of interest (ROIs): symmetrical areas of the frontal, parietal-occipital, and temporal lobes. Before the contrast CT scan, blood was drawn from the central artery of the ear for measurement of plasma NO. RESULTS: In the control group, there was no difference in CBV, CBF, and MTT in the six ROIs, and plasma NO was unchanged. Compared to controls, in the SAH group, CBV decreased slightly in the six ROIs (P > 0.05), frontal lobe CBF decreased, MTT increased (P < 0.05, for both), and NO plasma levels were significantly lower (P < 0.01). CONCLUSIONS: There was a significant correlation between the increase in MTT and the decrease in plasma NO (P < 0.05), We hypothesized that normalization of NO might have a positive influence on brain microcirculation following SAH.

Dynamic Organization of Large-scale Functional Brain Networks Supports Interactions Between Emotion and Executive Control.

Emotion and executive control are often conceptualized as two distinct modes of human brain functioning. Little, however, is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control, especially their interactions. The amygdala and prefrontal systems have long been thought to play crucial roles in these processes. Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion, executive control, and their interactions. Here, we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches. In this model, dynamic interactions among the executive control network, the salience network, the default mode network, and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes; neural oscillations across multiple frequency bands and the locus coeruleus-norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks. This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.

Visual learning in a virtual reality environment upregulates immediate early gene expression in the mushroom bodies of honey bees.

Free-flying bees learn efficiently to solve numerous visual tasks. Yet, the neural underpinnings of this capacity remain unexplored. We used a 3D virtual reality (VR) environment to study visual learning and determine if it leads to changes in immediate early gene (IEG) expression in specific areas of the bee brain. We focused on kakusei, Hr38 and Egr1, three IEGs that have been related to bee foraging and orientation, and compared their relative expression in the calyces of the mushroom bodies, the optic lobes and the rest of the brain after color discrimination learning. Bees learned to discriminate virtual stimuli displaying different colors and retained the information learned. Successful learners exhibited Egr1 upregulation only in the calyces of the mushroom bodies, thus uncovering a privileged involvement of these brain regions in associative color learning and the usefulness of Egr1 as a marker of neural activity induced by this phenomenon.

The Neural Signature of Visual Learning Under Restrictive Virtual-Reality Conditions.

Honey bees are reputed for their remarkable visual learning and navigation capabilities. These capacities can be studied in virtual reality (VR) environments, which allow studying performances of tethered animals in stationary flight or walk under full control of the sensory environment. Here, we used a 2D VR setup in which a tethered bee walking stationary under restrictive closed-loop conditions learned to discriminate vertical rectangles differing in color and reinforcing outcome. Closed-loop conditions restricted stimulus control to lateral displacements. Consistently with prior VR analyses, bees learned to discriminate the trained stimuli. Ex vivo analyses on the brains of learners and non-learners showed that successful learning led to a downregulation of three immediate early genes in the main regions of the visual circuit, the optic lobes (OLs) and the calyces of the mushroom bodies (MBs). While Egr1 was downregulated in the OLs, Hr38 and kakusei were coincidently downregulated in the calyces of the MBs. Our work thus reveals that color discrimination learning induced a neural signature distributed along the sequential pathway of color processing that is consistent with an inhibitory trace. This trace may relate to the motor patterns required to solve the discrimination task, which are different from those underlying pathfinding in 3D VR scenarios allowing for navigation and exploratory learning and which lead to IEG upregulation.

Transcriptional Profiles of Diploid Mutant Apis mellifera Embryos after Knockout of csd by CRISPR/Cas9.

In honey bees, complementary sex determiner (csd) is the primary signal of sex determination. Its allelic composition is heterozygous in females, and hemizygous or homozygous in males. To explore the transcriptome differences after sex differentiation between males and females, with genetic differences excluded, csd in fertilized embryos was knocked out by CRISPR/Cas9. The diploid mutant males at 24 h, 48 h, 72 h, and 96 h after egg laying (AEL) and the mock-treated females derived from the same fertilized queen were investigated through RNA-seq. Mutations were detected in the target sequence in diploid mutants. The diploid mutant drones had typical male morphological characteristics and gonads. Transcriptome analysis showed that several female-biased genes, such as worker-enriched antennal (Wat), vitellogenin (Vg), and some venom-related genes, were down-regulated in the diploid mutant males. In contrast, some male-biased genes, such as takeout and apolipophorin-III-like protein (A4), had higher expressions in the diploid mutant males. Weighted gene co-expression network analysis (WGCNA) indicated that there might be interactions between csd and fruitless (fru), feminizer (fem) and hexamerin 70c (hex70c), transformer-2 (tra2) and troponin T (TpnT). The information provided by this study will benefit further research on the sex dimorphism and development of honey bees and other insects in Hymenoptera.

White matter tracts in Bipolar Disorder patients: A comparative study based on diffusion kurtosis and tensor imaging.

BACKGROUND: Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), is a powerful tool for studying human brain.The purpose is to investigate differences between DKI and DTI by comparing parameters in same analysis methods with bipolar disorder (BD) patients. METHODS: In this study, we attained in 47 BD patients and 49 age-, sex-, and education-matched healthy controls, complimented DTI and DKI scanning and got Fractional Anisotropy (FA), Mean Diffusion (MD) and Mean Kurtosis (MK). Voxel-wise statistical analysis was performed by the tract-based spatial statistics (TBSS) analysis and atlas-based regional data analysis. RESULTS: TBSS analysis showed more widespread regions and higher fidelity in DKI parameters than DTI parameters with the same p-value threshold, and DKI parameters showed significant alterations after Family-Wise Error correction. The DKI-FA value in the corpus callosum, bilateral cingulum (cingulate gyrus), bilateral superior corona radiata, left anterior corona radiata and left posterior corona radiata of BD patients was negatively correlated with the duration of illness. In the atlas-based regional data analysis, the effect size of DTI-FA, DTI-MD, DKI-FA and DKI-MD were quantified using Cohen's d value. DKI-FA and DKI-MD demonstrated more between-group different regions and the higher (p < 0.001) absolute Cohen's d value than DTI-FA. LIMITATIONS: This study did not consider the difference between sub-types of BD. CONCLUSIONS: Compared to DTI parameters, DKI parameters were more sensitive and stable to probe the local microstructure, and particularly powerful to exploit cerebral alterations in BD patients.

Abnormal dynamic resting-state brain network organization in auditory verbal hallucination.

Auditory-verbal hallucinations (AVH) are a key symptom of schizophrenia. Recent neuroimaging studies examining dynamic functional connectivity suggest that disrupted dynamic interactions between brain networks characterize complex symptoms in mental illness including schizophrenia. Studying dynamic connectivity may be especially relevant for hallucinations, given their fluctuating phenomenology. Indeed, it remains unknown whether AVH in schizophrenia are directly related to altered dynamic connectivity within and between key brain networks involved in auditory perception and language, emotion processing, and top-down control. In this study, we used dynamic connectivity approaches including sliding window and k-means to examine dynamic interactions among brain networks in schizophrenia patients with and without a recent history of AVH. Dynamic brain network analysis revealed that patients with AVH spent less time in a 'network-antagonistic' brain state where the default mode network (DMN) and the language network were anti-correlated, and had lower probability to switch into this brain state. Moreover, patients with AVH showed a lower connectivity within the language network and the auditory network, and lower connectivity was observed between the executive control and the language networks in certain dynamic states. Our study provides the first neuroimaging evidence of altered dynamic brain networks for understanding neural mechanisms of AVH in schizophrenia. The findings may inform and further strengthen cognitive models of AVH that aid the development of new coping strategies for patients.

Visualizing Sacbrood Virus of Honey Bees via Transformation and Coupling with Enhanced Green Fluorescent Protein.

Sacbrood virus (SBV) of honey bees is a picornavirus in the genus Iflavirus. Given its relatively small and simple genome structure, single positive-strand RNA with only one ORF, cloning the full genomic sequence is not difficult. However, adding nonsynonymous mutations to the bee iflavirus clone is difficult because of the lack of information about the viral protein processes. Furthermore, the addition of a reporter gene to the clones has never been accomplished. In preliminary trials, we found that the site between 3' untranslated region (UTR) and poly(A) can retain added sequences. We added enhanced green fluorescent protein (EGFP) expression at this site, creating a SBV clone with an expression tag that does not affect virus genes. An intergenic region internal ribosome entry site (IRES) from Black queen cell virus (BQCV) was inserted to initiate EGFP expression. The SBV-IRES-EGFP clone successfully infected Apis cerana and Apis mellifera, and in A. cerana larvae, it was isolated and passaged using oral inoculation. The inoculated larvae had higher mortality and the dead larvae showed sacbrood symptoms. The added IRES-EGFP remained in the clone through multiple passages and expressed the expected EGFP in all infected bees. We demonstrated the ability to add gene sequences in the site between 3'-UTR and poly(A) in SBV and the potential to do so in other bee iflaviruses; however, further investigations of the mechanisms are needed. A clone with a desired protein expression reporter will be a valuable tool in bee virus studies.

Association of LHPP genetic variation (rs35936514) with structural and functional connectivity of hippocampal-corticolimbic neural circuitry.

A single nucleotide polymorphism at the LHPP gene (rs35936514) has been reported to be associated with major depressive disorder (MDD) in genome-wide association studies. We conducted a neuroimaging analysis to explore whether and which brain neural systems are affected by LHPP variation. Since LHPP variants seem to be associated with the hippocampus, we assessed the relationship between rs35936514 variation and structural-functional connectivity within a hippocampal-corticolimbic neural system implicated in MDD. A total of 122 Chinese subjects were divided into a CC homozygous group (CC genotype, n = 60) and a T allele-carrier group (CT/TT genotypes, n = 62). All subjects participated in resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) scans. Structural and functional connectivity data analyses were then performed. Compared to the CC group, the T allele-carrier group showed significantly higher fractional anisotropy (FA) values in the fornix as well as increased functional connectivity from the hippocampus to the rostral part of the anterior cingulate cortex (rACC). Moreover, a significant negative correlation between fornix FA value and hippocampus-rACC functional connectivity was identified (P < 0.05). These findings suggest that there is a relationship between rs35936514 variation and both structural and functional hippocampal-corticolimbic neural system involvement in MDD. LHPP may play an important role in the neuropathophysiology of MDD.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma.

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Identification of diagnostic long non­coding RNA biomarkers in patients with hepatocellular carcinoma.

Liver cancer is a leading cause of cancer­associated mortality worldwide. Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The aim of the present study was to identify long non­coding RNA (lncRNAs) as diagnostic biomarkers for HCC. The lncRNA and mRNA expression profiles of a large group of patients with HCC were obtained from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) and the differentially expressed mRNAs (DEmRNAs) were identified by bioinformatics analysis. Using feature selection procedure and a classification model, the optimal diagnostic lncRNA biomarkers for HCC were identified. Classification models, including random forests, decision tree and support vector machine (SVM), were established to distinguish between HCC and normal tissues. DEmRNAs co­expressed with the lncRNAs were considered as targets of DElncRNAs. Functional annotation of DEmRNAs co­expressed with these lncRNAs biomarkers was performed. Receiver operating characteristic curve analysis of lncRNAs biomarkers was conducted. A total of 3,177 lncRNAs and 15,183 mRNAs between HCC and normal tissues were obtained. RP11­486O12.2, RP11­863K10.7, LINC01093 and RP11­273G15.2 were identified as optimal diagnostic lncRNA biomarkers for HCC that were co­expressed with 273, 69, 76 and 1 DEmRNAs, respectively. The area under the curve values of the random forest model, decision tree model and SVM model were 0.992, 0.927 and 0.992, and the specificity and sensitivity of the three models were 100.0 and 95.6, 92.0 and 98.3 and 98.0 and 97.2%, respectively. 'PPAR signaling pathway' and 'retinol metabolism' were two significantly enriched target pathways of DElncRNAs. The present study identified four DElncRNAs, including RP11­486O12.2, RP11­863K10.7, LINC01093 and RP11­273G15.2, as potential diagnostic biomarkers of HCC. Functional annotation of target DEmRNAs provided novel evidence for examining the precise roles of lncRNA in HCC.

Abnormal Functional and Structural Connectivity of Amygdala-Prefrontal Circuit in First-Episode Adolescent Depression: A Combined fMRI and DTI Study.

BACKGROUND: Abnormalities of functional and structural connectivity in the amygdala-prefrontal circuit which involved with emotion processing have been implicated in adults with major depressive disorder (MDD). Adolescent MDD may have severer dysfunction of emotion processing than adult MDD. In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) to examine the potential functional and structural connectivity abnormalities within amygdala-prefrontal circuit in first-episode medication-naïve adolescents with MDD. METHODS: Rs-fMRI and DTI data were acquired from 36 first-episode medication-naïve MDD adolescents and 37 healthy controls (HC). Functional connectivity between amygdala and the prefrontal cortex (PFC) and fractional anisotropy (FA) values of the uncinate fasciculus (UF) which connecting amygdala and PFC were compared between the MDD and HC groups. The correlation between the FA value of UF and the strength of the functional connectivity in the PFC showing significant differences between the two groups was identified. RESULTS: Compared with the HC group, decreased functional connectivity between left amygdala and left ventral PFC was detected in the adolescent MDD group. FA values were significant lower in the left UF within the adolescent MDD group compared to the HC group. There was no significant correlation between the UF and FA, and the strength of functional connectivity within the adolescent MDD group. CONCLUSIONS: First-episode medication-naïve adolescent MDD showed decreased functional and structural connectivity in the amygdala-prefrontal circuit. These findings suggest that both functional and structural abnormalities of the amygdala-prefrontal circuit may present in the early onset of adolescent MDD and play an important role in the neuropathophysiology of adolescent MDD.

Anticipation of Uncertain Threat Modulates Subsequent Affective Responses and Covariation Bias.

Uncertainty contributes to stress and anxiety-like behaviors by impairing the ability of participants to objectively estimate threat. Our study used the cue-picture paradigm in conjunction with the event-related potential (ERP) technique to explore the temporal dynamics of anticipation for and response to uncertain threat in healthy individuals. This task used two types of cue. While 'certain' cues precisely forecasted the valence of the subsequent pictures (negative or neutral), the valence of pictures following 'uncertain' cues was not predictable. ERP data showed that, during anticipation, uncertain cues elicited similar Stimulus-Preceding Negativity (SPN) to certain-negative cues, while both of them elicited larger SPN than certain-neutral cues. During affective processing, uncertainty enlarged the mean amplitude of late positive potential (LPP) for both negative and neutral pictures. Behavioral data showed that participants reported more negative mood ratings of uncertain-neutral pictures relative to certain-neutral pictures and overestimated the probability of negative pictures following uncertain cues. Importantly, the enlarged anticipatory activity evoked by uncertain cues relative to that evoked by certain-neutral cues positively modulated the more negative mood ratings of uncertain-neutral pictures relative to certain-neutral pictures. Further, this more negative mood ratings and the general arousal anticipation during anticipatory stage contributed to the covariation bias. These results can provide a novel insight into understanding the neural mechanism and pathological basis of anxiety.

Genome-Wide Identification and Characterization of Fox Genes in the Honeybee, Apis cerana, and Comparative Analysis with Other Bee Fox Genes.

The forkhead box (Fox) gene family, one of the most important families of transcription factors, participates in various biological processes. However, Fox genes in Hymenoptera are still poorly known. In this study, 14 Fox genes were identified in the genome of Apis cerana. In addition, 16 (Apis mellifera), 13 (Apis dorsata), 16 (Apis florea), 17 (Bombus terrestris), 16 (Bombus impatiens), and 18 (Megachile rotundata) Fox genes were identified in their genomes, respectively. Phylogenetic analyses suggest that FoxA is absent in the genome of A. dorsata genome. Similarly, FoxG is missing in the genomes A. cerana and A. dorsata. Temporal expression profiles obtained by quantitative real-time PCR revealed that Fox genes have distinct expression patterns in A. cerana, especially for three genes ACSNU03719T0 (AcFoxN4), ACSNU05765T0 (AcFoxB), and ACSNU07465T0 (AcFoxL2), which displayed high expression at the egg stage. Tissue expression patterns showed that FoxJ1 is significantly higher in the antennae of A. cerana and A. mellifera compared to other tissues. These results may facilitate a better understanding of the potential physiological functions of the Fox gene family in A. cerana and provide valuable information for a comprehensive functional analysis of the Fox gene family in Hymenopterans.