Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

Dysfunction of AMPA receptor GluA3 is associated with aggressive behavior in human.

Inappropriate aggression in humans hurts the society, families and individuals. The genetic basis for aggressive behavior, however, remains largely elusive. In this study, we identified two rare missense variants in X-linked GRIA3 from male patients who showed syndromes featuring aggressive outbursts. Both G630R and E787G mutations in AMPA receptor GluA3 completely lost their ion channel functions. Furthermore, a guanine-repeat single nucleotide polymorphism (SNP, rs3216834) located in the first intron of human GRIA3 gene was found to regulate GluA3 expression with longer guanine repeats (rs3216834-10G/-11G) suppressing transcription compared to the shorter ones (-7G/-8G/-9G). Importantly, the distribution of rs3216834-10G/-11G was elevated in a male violent criminal sample from Chinese Han population. Using GluA3 knockout mice, we showed that the excitatory neurotransmission and neuronal activity in the medial prefrontal cortex (mPFC) was impaired. Expressing GluA3 back into the mPFC alleviated the aggressive behavior of GluA3 knockout mice, suggesting that the defects in mPFC explained, at least partially, the neural mechanisms underlying the aggressive behavior. Therefore, our study provides compelling evidence that dysfunction of AMPA receptor GluA3 promotes aggressive behavior.

[Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation].

The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What's more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.

Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI.

BACKGROUND: Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level. METHODS: 24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features. RESULTS: The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia. CONCLUSIONS: This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.

Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering.

BACKGROUND: Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences. METHODS: 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives. RESULTS: Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms. CONCLUSIONS: These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.

Cerebrotendinous xanthomatosis presenting with schizophrenia-like disorder: A case report.

BACKGROUND: Cerebrotendinous xanthomatosis (CTX) is a rare autosomal recessive lipid-storage disorder caused by mutations in CYP27A1. Psychiatric manifestations in CTX are rare and nonspecific, and they often lead to considerable diagnostic and treatment delay. CASE SUMMARY: A 33-year-old female patient admitted to the psychiatric ward for presentation of delusions, hallucinations, and behavioral disturbance is reported. The patient presented with cholestasis, cataract, Achilles tendon xanthoma, and cerebellar signs in adulthood and with intellectual disability and learning difficulties in childhood. After the characteristic CTX findings on imaging were obtained, a pathological examination of the Achilles tendon xanthoma was refined. Re-placement therapy was then initiated after the diagnosis was clarified by genetic analysis. During hospitalization in the psychiatric ward, the nonspecific psychiatric manifestations of the patient posed difficulty in diagnosis. After the patient's history of CTX was identified, the patient was diagnosed with organic schizophrenia-like disorder, and psychotic symptoms were controlled by replacement therapy combined with antipsychotic medication. CONCLUSION: Psychiatrists should be aware of CTX, its psychiatric manifestations, and clinical features and avoid misdiagnosis of CTX for timely intervention.

Altered brain regional homogeneity is associated with cognitive dysfunction in first-episode drug-naive major depressive disorder: A resting-state fMRI study.

BACKGROUND: Our study aimed to explore the abnormal spontaneous brain activity by regional homogeneity (ReHo) and its association with cognitive function to understand the neuropathology of major depressive disorder (MDD). METHODS: ReHo was used to investigate brain activities of 60 patients with first-episode drug-naive MDD and 60 healthy controls (HCs). Partial correlation analysis was conducted on altered ReHo values and the severity of symptoms and cognitive deficits. Moreover, support vector machine analysis was used to evaluate the accuracy of abnormal ReHo values in distinguishing patients with MDD from HCs. RESULTS: Compared with HCs, patients with MDD showed significantly increased ReHo values in the right cerebellum crus2 and right thalamus and decreased ReHo values in the right angular gyrus (AG) and right precuneus (PCUN). The ReHo values in right cerebellum crus2 and right AG were positively associated with working memory and visual learning, respectively. Furthermore, the combination of ReHo values in the right cerebellum crus2 and right PCUN discriminated the patients with MDD from HCs with specificity, sensitivity, and accuracy of 0.9688, 0.6250, and 0.90, respectively. LIMITATIONS: The design of repeated cross-sectional surveys does not allow analyses of within individual changes. CONCLUSIONS: Our study revealed that the pathophysiology mechanism of cognitive deficits in MDD may be related to abnormal spontaneous brain activity. Moreover, the combination of ReHo values in the right cerebellum crus2 and right PCUN can be used to discriminate patients with MDD from HCs effectively.

Mutation of the attractin gene impairs working memory in rats.

OBJECTIVE: Attractin (ATRN) is a widely expressed member of the cell adhesion and guidance protein family in humans that is closely related to cellular immunity and neurodevelopment. However, while previous studies in our laboratory have confirmed the effect of ATRN mutations on long-term memory, its specific role and the molecular mechanism by which it influences spatial cognition are poorly understood. METHODS: This study aimed to examine the effect of ATRN mutations on working memory in water maze with a novel ATRN-mutant rat generated by the CRISPR/Cas9 system; the mutation involved the substitution of the 505th amino acid, glycine (G), with cysteine (C), namely, a mutation from GGC to TGC. The changes in myelin basic protein (MBP) expression in rats were also analyzed with the western blot. RESULTS: The ATRN-G505C(KI/KI) rats exhibited significant increases in the required latency and distance traveled to locate the escape platform in a Morris water maze test of working memory. In addition, the expression of MBP was reduced in ATRN-mutant rats, as shown in the western blot analysis. CONCLUSION: Our results indicate that ATRN gene mutations may directly lead to the impairment of working memory in the water maze; this impairment may be due to the inhibition of MBP expression, which in turn affects the spatial cognition.

Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders.

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.

Stacked Topological Preserving Dynamic Brain Networks Representation and Classification.

In recent years, numerous studies have adopted rs-fMRI to construct dynamic functional connectivity networks (DFCNs) and applied them to the diagnosis of brain diseases, such as epilepsy and schizophrenia. Compared with the static brain networks, the DFCNs have a natural advantage in reflecting the process of brain activity due to the time information contained in it. However, most of the current methods for constructing DFCNs fail to aggregate the brain topology structure and temporal variation of the functional architecture associated with brain regions, and often ignore the inherent multi-dimensional feature representation of DFCNs for classification. In order to address these issues, we propose a novel DFCNs construction and representation method and apply it to brain disease diagnosis. Specifically, we fuse the blood oxygen level dependent (BOLD) signal and interactions between brain regions to distinguish the brain topology within each time domain and across different time domains, by embedding block structure in the adjacency matrix. After that, a sparse tensor decomposition method with sparse local structure preserving regularization is developed to extract DFCNs features from a multi-dimensional perspective. Finally, the kernel discriminant analysis is employed to provide the decision result. We validate the proposed method on epilepsy and schizophrenia identification tasks, respectively. The experimental results show that the proposed method outperforms several state-of-the-art methods in the diagnosis of brain diseases.

Abnormal network homogeneity of default-mode network and its relationships with clinical symptoms in antipsychotic-naïve first-diagnosis schizophrenia.

Schizophrenia is a severe mental disorder affecting around 0.5-1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients' age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.

Association of Reduced Local Activities in the Default Mode and Sensorimotor Networks with Clinical Characteristics in First-diagnosed Episode of Schizophrenia.

The neurodevelopmental hypothesis states that schizophrenia is a brain disease. Exploring abnormal brain activities can improve understanding of the neural pathologic mechanism of clinical characteristics and determine subjective biomarkers to differentiate patients with schizophrenia from healthy controls. We collected clinical characteristics (i.e., demographics, positive and negative syndrome scale (PANSS) scores, and cognitive scores) and magnetic resonance imaging (MRI) data from 57 first-diagnosed drug-naïve patients with schizophrenia and 50 healthy controls. The fractional amplitude of low-frequency fluctuation (fALFF) was used to detect local activities. Partial correlation analysis was applied to estimate the relationship between abnormal regions and clinical characteristics. The support vector machine (SVM) analysis was used to calculate the accuracy of classification in abnormal regions. In our study, the fALFF values in the right postcentral gyrus, left precentral gyrus/postcentral gyrus, left postcentral gyrus/superior parietal lobule, bilateral supplementarymotor area, bilateral paracentral lobule, and bilateral precuneus were decreased in patients with schizophrenia and associated with clinical characteristics. However, the related patterns of cognition of patients were different from those of healthy controls. Additionally, the combination of fALFF values in the bilateral paracentral lobule and right postcentral gyrus might distinguish patients with schizophrenia from healthy controls with high accuracy (98.13%), specificity (98.00%), and sensitivity (98.25%). Our study suggests that reduced local activities in the default mode network and sensorimotor network may be regarded as neural underpinnings of clinical characteristics and may discriminate patients with schizophrenia from healthy controls.

Shared genetics between autism spectrum disorder and attention-deficit/hyperactivity disorder and their association with extraversion.

BACKGROUND: Deciphering the genetic relationships between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) may uncover underlining shared pathophysiology as well as inform treatment. METHODS: The summary results of genome-wide association studies on ADHD, ASD, and extraversion were utilized for the analyzes. Genetic correlations between ADHD, ASD, and extraversion were tested using linkage disequilibrium score regression. Causal relationships between ADHD, ASD, and extraversion were investigated using Mendelian randomization (MR) analysis. Novel pleiotropic genomic loci shared by ADHD and ASD were identified using a cross-trait meta-analysis. RESULTS: Extraversion was positively correlated with ADHD (rg = 0.205) and negatively correlated with ASD (rg = -0.193). The MR analysis showed that ADHD confers a causal effect on ASD (OR: 1.35, 95% confidence interval (CI):1.20-1.52) and vice versa (1.46, 1.38-1.55). Extraversion exerts a causal effect on ADHD only (1.19, 1.05-1.33). The cross-trait meta-analysis identified three novel pleiotropic genomic loci for ADHD and ASD, involving two pleiotropic genes, LINC00461 and KIZ. CONCLUSIONS: Our study provides new insights into the shared genetics of ADHD and ASD and their connections with extraversion.

Altered regional homogeneity and cognitive impairments in first-episode schizophrenia: A resting-state fMRI study.

BACKGROUND: Patients with schizophrenia consistently present pervasive cognitive deficits, but the neurobiological mechanism of cognitive impairments remains unclear. By analyzing regional homogeneity (ReHo) of resting-state functional Magnetic Resonance Imaging, this study aimed to explore the association between brain functional alterations and cognitive deficits in first-episode schizophrenia (FES) with a relatively large sample. METHODS: A total of 187 patients with FES and 100 healthy controls from 3 independent cohorts underwent resting-state functional magnetic resonance scans. The MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive function. Partial correlation analysis was performed between abnormal ReHo values and the severity of symptoms and cognitive deficits. RESULTS: Compared with healthy controls, ReHo values increased in right superior frontal cortex and decreased in right anterior cingulate cortex (ACC), left middle occipital gyrus (MOG), left cuneus, right posterior cingulate cortex (PCC), and right superior occipital gyrus in schizophrenia patients. ReHo values in ACC, PCC and superior occipital gyrus were correlated with PANSS scores. In addition, ReHo values in ACC and MOG were negatively correlated with working memory; left cuneus was positively correlated with multiple cognitive domains (speed of processing, attention/vigilance and social cognition); PCC was positively correlated with verbal learning; right superior occipital gyrus was positively correlated with speed of processing and social cognition. CONCLUSION: In conclusion, we found widespread ReHo alterations and cognitive dysfunction in FES. And the pathophysiology mechanism of a wide range of cognitive deficits may be related to abnormal spontaneous brain activity.

Identifying and revealing different brain neural activities of cognitive subtypes in early course schizophrenia.

Background: Cognitive subtypes of schizophrenia may exhibit different neurobiological characteristics. This study aimed to reveal the underlying neurobiological features between cognitive subtypes in the early course of schizophrenia (ECS). According to prior studies, we hypothesized to identify 2-4 distinct cognitive subtypes. We further hypothesized that the subtype with relatively poorer cognitive function might have lower brain spontaneous neural activity than the subtype with relatively better cognitive function. Method: Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Resting-state functional magnetic resonance imaging scanning was conducted for each individual. There were 155 ECS individuals and 97 healthy controls (HCs) included in the subsequent analysis. Latent profile analysis (LPA) was used to identify the cognitive subtypes in ECS individuals, and amplitude of low-frequency fluctuations (ALFFs) was used to measure brain spontaneous neural activity in ECS individuals and HCs. Results: LPA identified two cognitive subtypes in ECS individuals, containing a severely impaired subtype (SI, n = 63) and a moderately impaired subtype (MI, n = 92). Compared to HCs, ECS individuals exhibited significantly increased ALFF in the left caudate and bilateral thalamus and decreased ALFF in the bilateral medial prefrontal cortex and bilateral posterior cingulate cortex/precuneus (PCC/PCu). In ECS cognitive subtypes, SI showed significantly higher ALFF in the left precentral gyrus (PreCG) and lower ALFF in the left PCC/PCu than MI. Furthermore, ALFFs of left PreCG were negatively correlated with several MCCB cognitive domains in ECS individuals, while ALFF of left PCC/PCu presented opposite correlations. Conclusion: Our findings suggest that differences in the brain spontaneous neural activity of PreCG and PCC/PCu might be the potential neurobiological features of the cognitive subtypes in ECS, which may deepen our understanding of the role of PreCG and PCC/PCu in the pathogenesis of cognitive impairment in schizophrenia.

Genetic evidence suggests posttraumatic stress disorder as a subtype of major depressive disorder.

BACKGROUNDMajor depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression).METHODSGenetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis.RESULTSGenetic correlations of PTSD with the depressive phenotypes were in the range of 0.71-0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21-0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD.CONCLUSIONEvidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology.FUNDINGThe National Key Research and Development Program of China and the National Natural Science Foundation of China.

An Empirical Comparative Study on the Two Methods of Eliciting Singers' Emotions in Singing: Self-Imagination and VR Training.

Emotional singing can affect vocal performance and the audience's engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing. Recently, virtual reality (VR) technologies have been applied in several fields for training purposes. In this empirical comparative study, a VR training task was implemented to elicit emotions from singers and further assist them with improving their emotional singing performance. The VR training method was compared against the traditional self-imagination method. By conducting a two-stage experiment, the two methods were compared in terms of emotions' elicitation and emotional singing performance. In the first stage, electroencephalographic (EEG) data were collected from the subjects. In the second stage, self-rating reports and third-party teachers' evaluations were collected. The EEG data were analyzed by adopting the max-relevance and min-redundancy algorithm for feature selection and the support vector machine (SVM) for emotion recognition. Based on the results of EEG emotion classification and subjective scale, VR can better elicit the positive, neutral, and negative emotional states from the singers than not using this technology (i.e., self-imagination). Furthermore, due to the improvement of emotional activation, VR brings the improvement of singing performance. The VR hence appears to be an effective approach that may improve and complement the available vocal music teaching methods.

Attractin Participates in Schizophrenia by Affecting Testosterone Levels.

Attractin (ATRN) is a widely expressed glycoprotein that is involved in energy homeostasis, neurodevelopment, and immune response. It is encoded by a gene spanning 180 kb on chromosome 20p13, a region previously implicated in schizophrenia by linkage studies. To address a possible role of ATRN in disorders of the central nervous system, we created an atrn knockout zebrafish line and performed behavioral tests. Adult atrn-/- zebrafish exhibited more pronounced attack behavior relative to wild-type control zebrafish in a tracking analysis. Biochemical analysis revealed elevated testosterone levels in atrn-/- zebrafish. At the gene expression level, we noted an upregulation of cyp51 and hsd17b7, key proteins in testosterone synthesis in the brains of both adult and larvae of atrn-/- zebrafish. In order to further elucidate the relationship between testosterone and behavioral syndromes, we then compared testosterone levels of 9,008 psychiatric patients and 247 healthy controls from the same catchment area. Of all subjects examined, male subjects with schizophrenia exhibited lower testosterone levels compared with controls. In contrast, female subjects with a diagnosis of schizophrenia or bipolar disorder featured higher testosterone levels than did same sex controls. Purposeful sampling of extreme groups showed reduced ATRN expression in a subset of these subjects. Finally, we identified 14 subjects with ATRN mutations. All of whom displayed abnormal testosterone levels. In summary, the interplay of ATRN and testosterone may help to explain sexual dimorphisms in selected behavioral phenotypes.

Attractin Gene Deficiency in Rats Leads to Impairments in Both Activity and Spatial Learning and Memory.

Attractin (ATRN), an autosomal recessive gene that is widely distributed in the brain, is involved in the execution of a variety of brain functions and associated with certain neuropsychiatric disorders. Here, we introduce a novel rat strain harboring a mutation in ATRN that was generated by knocking in ATRN-G505C via the CRISPR/Cas9 system. We assessed the behavioral performance of these mutant ATRN knock-in rats. The G505C mutation was introduced into exon 9, and a synthetic primer was inserted into introns 8-9 for genotyping. The 505th amino acid, a Gly (G) residue, was mutated to a Cys (C) residue, i.e., GGC was mutated to TGC. Behavioral experiments showed that homozygous ATRN rats spent significantly more time searching for the escape platform in the acquisition trial and significantly less time in the target area in the probe trial in the Morris water maze (MWM) test and traveled a significantly shorter distance in the open field test (OFT) than wild-type rats. In addition, Western blot analysis and immunohistochemistry showed that rats with the mutant ATRN gene exhibited significantly reduced expression of brain-derived neurotrophic factor (BDNF). In summary, our results indicate that mutations in the ATRN gene directly lead to learning and memory impairments and slight motor deficits. These findings provide new clues for the mechanism by which mutant ATRN induces neurodegenerative changes.

A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis.

Brain networks based on various neuroimaging technologies, such as diffusion tensor image (DTI) and functional magnetic resonance imaging (fMRI), have been widely applied to brain disease analysis. Currently, there are several node-level structural measures (e.g., local clustering coefficients and node degrees) for representing and analyzing brain networks since they usually can reflect the topological structure of brain regions. However, these measures typically describe specific types of structural information, ignoring important network properties (i.e., small structural changes) that could further improve the performance of brain network analysis. To overcome this problem, in this paper, we first define a novel node-level structure embedding and alignment (nSEA) representation to accurately characterize the node-level structural information of the brain network. Different from existing measures that characterize a specific type of structural properties with a single value, our proposed nSEA method can learn a vector representation for each node, thus contain richer structure information to capture small structural changes. Furthermore, we develop an nSEA representation based learning (nSEAL) framework for brain disease analysis. Specifically, we first perform structural embedding to calculate node vector representations for each brain network and then align vector representations of all brain networks into the common space for two group-level network analyses, including a statistical analysis and brain disease classifications. Experiment results on a real schizophrenia dataset demonstrate that our proposed method not only discover disease-related brain regions that could help to better understand the pathology of brain diseases, but also improve the classification performance of brain diseases, compared with state-of-the-art methods.