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ABSTRACT Food aroma molecules have the potential to influence people's emotions through the olfactory pathway and are anticipated to emerge as a new method for regulating emotional states, owing to their simplicity and high acceptance. Current research on food aroma predominantly centres on the physicochemical properties and formation mechanisms of aroma components, neglecting the effects of aroma molecules in emotional regulation. Moreover, the evaluation of pleasantness, a pivotal dimension of emotions, lacks objective assessment methods. In this study, sensory assessments of pleasantness for 12 aroma compounds were gathered from 45 subjects, and their correlation with the brain's activity responses in the left frontal‐temporal lobe (LFT) and right frontal‐temporal lobe (RFT) using electroencephalogram (EEG) signals was analysed. The results revealed a close relationship between brain activity in the LFT and the perception of aroma pleasantness. Furthermore, a substantial correlation was observed between the α, β and γ frequency bands in the LFT and the subjective pleasantness scores. These findings demonstrate that the LFT plays a critical role in evaluating the pleasantness of aroma molecules, and that changes in the power of the α, β and γ bands serve as important evaluation indicators. Consequently, this method offers a new objective means for assessing pleasantness to find higher pleasantness aroma molecules and the emotional regulation of food aroma. [ABSTRACT FROM AUTHOR]

AIM: To study functional brain abnormalities in patients with hypertensive retinopathy (HR) and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations (fALFFs) method. METHODS: Twenty HR patients and 20 healthy controls (HCs) were respectively recruited. The age, gender, and educational background characteristics of the two groups were similar. After functional magnetic resonance imaging (fMRI) scanning, the subjects' spontaneous brain activity was evaluated with the fALFF method. Receiver operating characteristic (ROC) curve analysis was used to classify the data. Further, we used Pearson's correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR. RESULTS: The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus (RO-MFG) and right lingual gyrus. In contrast, the values of fALFFs in the left middle temporal gyrus (MTG), left superior temporal pole (STP), left middle frontal gyrus (MFG), left superior marginal gyrus (SMG), left superior parietal lobule (SPL), and right supplementary motor area (SMA) were higher in the HR group. The results of a t-test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group (P

AIM: To study functional brain abnormalities in patients with eye trauma (ET) and to discuss the pathophysiological mechanisms of ET. METHODS: Totally 31 ET patients and 31 healthy controls (HCs) were recruited. The age, gender, and educational background characteristics of the two groups were similar. After functional magnetic resonance imaging (fMRI) scanning, the subjects' spontaneous brain activity was evaluated with the functional connectivity (FC) method. Receiver operating characteristic (ROC) curve analysis was used to classify the data. Pearson's correlation analysis was used to explore the relationship between FC values in specific brain regions and clinical behaviors in patients with ET. RESULTS: Significantly increased FC between several regions was identified including the medial prefrontal cortex (MPFC) and left hippocampus formations (HF), the MPFC and left inferior parietal lobule (IPL), the left IPL and left medial temporal lobe (MTL), the left IPL and right MTL, and the right IPL and left MTL. No decreased region-to-region connectivity was detected in default mode network (DMN) sub-regions in patients with ET. Compared with HCs, ET patients exhibited significantly increased FC between several paired DMN regions, as follows: posterior cingulate cortex (PCC) and right HF (HF.R, t=2.196, P=0.032), right inferior parietal cortices (IPC.R) and left MTL (MTL.L, t=2.243, P=0.029), and right MTL (MTL.R) and HF.R (t=2.236, P=0.029). CONCLUSION: FC values in multiple brain regions of ET patients are abnormal, suggesting that these brain regions in ET patients may be dysfunctional, which may help to reveal the pathophysiological mechanisms of ET.

Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities. This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals. In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction. The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection. The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis. [ABSTRACT FROM AUTHOR]

Abstract Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.

Postherpetic neuralgia (PHN) is a typical chronic neuropathic pain syndrome. Both peripheral and central nervous system mechanisms are believed to be involved in PHN, but the central nervous system-related brain network structure and function are not yet fully elucidated, limiting the study on the clinical analgesic drugs and other intervention strategies. In recent years, the research on pain matrix-related brain networks has helped to reveal the central nervous system regulation mechanism of pain, but there are few reports on the PHN pain matrix. This review summarizes the recent studies on the PHN pain matrix, retrospectively analyzes the functional and structural changes in specific pain-related brain regions, in order to provide the new insights for exploring the effective targeted analgesic treatments. [ABSTRACT FROM AUTHOR]

Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Highlights: Subject independent human emotion identification is studied using SEED data set. Tsallis entropy is employed as feature and performance variation with Tsallis parameter (q = 2, 3, 4) is examined. Performance of kNN classifier is examined with Tsallis entropy feature. Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere. Prospects of TsEn-based real-time emotion recognition framework is canvassed. [ABSTRACT FROM AUTHOR]

Research on emotion recognition based on electroencephalogram (EEG) signals is important for human emotion detection and improvements in mental health. However, the importance of EEG signals from different brain regions and frequency bands for emotion recognition is different. For this problem, this paper proposes the Capsule–Transformer method for multi-region and multi-band EEG emotion recognition. First, the EEG features are extracted from different brain regions and frequency bands and combined into feature vectors which are input into the fully connected network for feature dimension alignment. Then, the feature vectors are inputted into the Transformer for calculating the self-attention of EEG features among different brain regions and frequency bands to obtain contextual information. Finally, utilizing capsule networks captures the intrinsic relationship between local and global features. It merges features from different brain regions and frequency bands, adaptively computing weights for each brain region and frequency band. Based on the DEAP dataset, experiments show that the Capsule–Transformer method achieves average classification accuracies of 96.75%, 96.88%, and 96.25% on the valence, arousal, and dominance dimensions, respectively. Furthermore, in emotion recognition experiments conducted on individual brain regions or frequency bands, it was observed that the frontal lobe exhibits the highest average classification accuracy, followed by the parietal, temporal, and occipital lobes. Additionally, emotion recognition performance is superior for high-frequency band EEG signals compared to low-frequency band signals. [ABSTRACT FROM AUTHOR]

Bin Wei,* Wen-Wen Fu,* Yu Ji, Qi Cheng, Ben-Liang Shu, Qin-Yi Huang, Xiao-Rong Wu Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-Rong Wu, Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330000, People’s Republic of China, Email wxr98021@126.comAim: The objective of this study was to examine changes in functional connectivity (FC) in the hippocampus among patients with high myopia (HM) compared to healthy controls (HCs) through the utilization of seed-based functional connectivity (FC) analysis.Methods: Resting-state functional magnetic resonance imaging (fMRI) was conducted on a sample of 82 patients diagnosed with high myopia (HM) and 59 HCs. The two groups were matched based on age, weight and other relevant variables. Using seed-based FC analysis to detect alterations in hippocampal FC patterns in HM patients and HCs. Furthermore, a correlation analysis was performed to examine the associations between the mean functional connectivity (FC) signals in various brain regions of patients with HM and their corresponding clinical manifestations.Results: The FC values in the left temporal pole-temporal gyrus (L-TPOsup), right hippocampus (R-HIP), left medial temporal gyrus (L-MTG) and left hippocampus in HM patients were significantly lower than those of healthy subjects. In the left temporal pole-superior temporal gyrus (L-TPOsup), right orbital part of middle frontal gyrus (RO-MFG), left fusiform gyrus (L-FG), left cerebellum superior (L-Cbe6), left middle temporal gyrus (L-MTG), right thalamus (R-THA), and right hippocampus, FC values were also significantly lower.Conclusion: Brain dysfunction was observed in various regions of the HM patients, suggesting the existence of neurobiological alterations that could lead to impairments in visual cognition, movement, emotional processing, and visual memory.Keywords: high myopia, hippocampus, resting state, brain function, brain region, functional connectivity

Xulin Liao,1,2 Chu Qi Li,1 Qian Min Ge,1 Li Ying Tang,3 Ting Su,4 Qiu Yu Li,1 Yi Cong Pan,1 Hui Ye Shu,1 Li Juan Zhang,1 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China; 2Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Kowloon, Hong Kong SAR, People’s Republic of China; 3Department of Ophthalmology, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, 361004, People’s Republic of China; 4Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, 02114, USACorrespondence: Yi Shao, Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, No. 17, Yong Wai Zheng Street, Dong Hu District, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel/Fax +86 791-88692520, Email freebee99@163.comPurpose: The purpose of this study was to use the percent amplitude of fluctuation (PerAF) to study the changes in brain activity and nerve function of herpes zoster keratitis (HZK) patients.Methods: We recruited 20 HZK patients and 20 healthy controls (HCs). Each of these groups included ten males and ten females and were matched in weight and age. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI). The percent amplitude of fluctuation (PerAF) method was used for analysis and detected differences between the two groups in the neurological function of brain areas. We also applied the receiver operating characteristic (ROC) curve to analyze the two groups and did a correlation analysis between the PerAF value, anxiety and depression score, and visual acuity.Results: The PerAF signal at the right putamen and right precentral gyrus was significantly higher in patients than in HCs. However, the PerAF value of the left inferior temporal was lower in patients than in HCs. In addition, the HZK patients’ anxiety and depression score (HADS) and visual acuity (V.A.) Log MAR negatively correlated with the PerAF value at the left inferior temporal gyrus.Conclusion: HZK patients had some changes in brain regions, and the changes were also related to their mood and visual acuity. These findings might contribute to other studies on the potential pathological mechanism, disease development, prognosis, and brain function in HZK patients.Keywords: herpes zoster keratitis, resting-state functional magnetic resonance imaging, brain activity patterns, brain region, the percent amplitude of fluctuation

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Substance use disorder (SUD) arises from the initiation to subsequent regular, irregular and harmful use of substances such as alcohol, tobacco/nicotine and cannabis. While thousands of genetic variants have been identified from recent large‐scale genome‐wide association studies (GWAS), understanding their functions in substance involvement and investigating the mechanisms by which they act in the addiction circuits remains challenging. In this study, we re‐analysed the brain regional transcriptome data from the most comprehensive postmortem transcriptomic study, with a focus on up‐ or down‐regulation of substance‐associated protein‐coding genes in the addiction circuit‐related brain regions (AddictRegions), including six cortical and 11 subcortical regions. We found that substance‐associated genes were overrepresented in AddictRegions. Interestingly, we observed a greater degree of genetic overlap between initiation and use and between use and SUD than between initiation and SUD. Moreover, substance initiation, use and SUD‐associated genes tend to shift their enriched AddictRegions from the cortical for initiation and, to a lesser extent, substance use to subcortical regions for SUD (e.g., thalamus, substantia nigra and ventral tegmental area). We also uncovered a pattern of coordinated cortical up‐regulation and subcortical down‐regulation for the genes associated with tobacco initiation and alcohol use. Moreover, the Gene Ontology terms of glutamate receptor activity and neurotransmitter binding were most significantly overrepresented in AddictRegion‐upregulated, substance‐associated genes, with the strongest enrichment for those involved in common substance use behaviours. Overall, our analysis provides a resource of AddictRegion‐related transcriptomes for studying substance‐associated genes and generates intriguing insights into the genetic control of substance initiation, use and SUD. [ABSTRACT FROM AUTHOR]

Acetamiprid (ACMP) is an extensively used neonicotinoid pesticide to control sucking and chewing insects and is known to cause nontarget toxicity. The present study aimed to evaluate the ameliorative potential of berberine (BBR)—a polyphenolic alkaloid‐ on ACMP‐induced oxidative stress, mitochondrial dysfunctioning, and structural changes in different rat brain regions. The male Wistar rats were divided into four groups, that is, control, BBR‐treated (150 mg/kg b.wt), ACMP‐exposed (21.7 mg/kg b.wt) and BBR + ACMP co‐treated; and were dosed intragastrically for 21 consecutive days. Results of the biochemical analysis showed that BBR significantly ameliorated ACMP‐induced oxidative stress by decreasing lipid peroxidation and protein oxidation along with a marked increase in endogenous antioxidants and lowered AChE activity in rat brain regions. Inside mitochondria, BBR significantly attenuated the toxic effects of ACMP by increasing the activity of mitochondrial complexes. Findings of polymerase chain reaction also demonstrated the modulatory effects of BBR against ACMP‐mediated downregulation of ND1, ND2, COX1, and COX4 subunits of mitochondrial complexes. The histopathological and ultrastructural examination also validated the biochemical and transcriptional alterations following toxicity of ACMP exposure and the protective potential of BBR against ACMP‐induced neurotoxicity. Thus, the present study indicates the promising ameliorative potential of BBR against ACMP‐induced neurotoxicity via its antioxidative and modulatory activities. [ABSTRACT FROM AUTHOR]

Background: There is increasing evidence that patients with retinal detachment (RD) have aberrant brain activity. However, neuroimaging investigations remain focused on static changes in brain activity among RD patients. There is limited knowledge regarding the characteristics of dynamic brain activity in RD patients. Aim: This study evaluated changes in dynamic brain activity among RD patients, using a dynamic amplitude of low-frequency fluctuation (dALFF), k-means clustering method and support vector machine (SVM) classification approach. Methods: We investigated inter-group disparities of dALFF indices under three different time window sizes using resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 RD patients and 24 demographically matched healthy controls (HCs). The k-means clustering method was performed to analyze specific dALFF states and related temporal properties. Additionally, we selected altered dALFF values under three distinct conditions as classification features for distinguishing RD patients from HCs using an SVM classifier. Results: RD patients exhibited dynamic changes in local intrinsic indicators of brain activity. Compared with HCs, RD patients displayed increased dALFF in the bilateral middle frontal gyrus, left putamen (Putamen_L), left superior occipital gyrus (Occipital_Sup_L), left middle occipital gyrus (Occipital_Mid_L), right calcarine (Calcarine_R), right middle temporal gyrus (Temporal_Mid_R), and right inferior frontal gyrus (Frontal_Inf_Tri_R). Additionally, RD patients showed significantly decreased dALFF values in the right superior parietal gyrus (Parietal_ Sup_R) and right paracentral lobule (Paracentral_Lobule_R) [two-tailed, voxellevel p < 0.05, Gaussian random field (GRF) correction, cluster-level p < 0.05]. For dALFF, we derived 3 or 4 states of ALFF that occurred repeatedly. There were differences in state distribution and state properties between RD and HC groups. The number of transitions between the dALFF states was higher in the RD group than in the HC group. Based on dALFF values in various brain regions, the overall accuracies of SVM classification were 97.87, 100, and 93.62% under three different time windows; area under the curve values were 0.99, 1.00, and 0.95, respectively. No correlation was found between hamilton anxiety (HAMA) scores and regional dALFF. Conclusion: Our findings offer important insights concerning the neuropathology that underlies RD and provide robust evidence that dALFF, a local indicator of brain activity, may be useful for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. [ABSTRACT FROM AUTHOR]

Cerebral microbleeds (CMB) are a common MRI finding, representing underlying cerebral microhemorrhages (CMH). The etiology of CMB and microhemorrhages is obscure. We conducted a pathological investigation of CMH, combining standard and immunohistological analyses of postmortem human brains. We analyzed 5 brain regions (middle frontal gyrus, occipital pole, rostral cingulate cortex, caudal cingulate cortex, and basal ganglia) of 76 brain bank subjects (mean age ± SE 90 ± 1.4 years). Prussian blue positivity, used as an index of CMH, was subjected to quantitative analysis for all 5 brain regions. Brains from the top and bottom quartiles (n = 19 each) were compared for quantitative immunohistological findings of smooth muscle actin, claudin-5, and fibrinogen, and for Sclerosis Index (SI) (a measure of arteriolar remodeling). Brains in the top quartile (i.e. with most extensive CMH) had significantly higher SI in the 5 brain regions combined (0.379 ± 0.007 vs 0.355 ± 0.008; p

Research on emotion recognition based on electroencephalogram (EEG) signals is important for human emotion detection and improvements in mental health. However, the importance of EEG signals from different brain regions and frequency bands for emotion recognition is different. For this problem, this paper proposes the Capsule–Transformer method for multi-region and multi-band EEG emotion recognition. First, the EEG features are extracted from different brain regions and frequency bands and combined into feature vectors which are input into the fully connected network for feature dimension alignment. Then, the feature vectors are inputted into the Transformer for calculating the self-attention of EEG features among different brain regions and frequency bands to obtain contextual information. Finally, utilizing capsule networks captures the intrinsic relationship between local and global features. It merges features from different brain regions and frequency bands, adaptively computing weights for each brain region and frequency band. Based on the DEAP dataset, experiments show that the Capsule–Transformer method achieves average classification accuracies of 96.75%, 96.88%, and 96.25% on the valence, arousal, and dominance dimensions, respectively. Furthermore, in emotion recognition experiments conducted on individual brain regions or frequency bands, it was observed that the frontal lobe exhibits the highest average classification accuracy, followed by the parietal, temporal, and occipital lobes. Additionally, emotion recognition performance is superior for high-frequency band EEG signals compared to low-frequency band signals.

Aim: This study was conducted to explore differences in static functional connectivity (sFC) and dynamic functional connectivity (dFC) alteration patterns in the primary visual area (V1) among high myopia (HM) patients and healthy controls (HCs) via seed-based functional connectivity (FC) analysis. Methods: Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 82 HM patients and 59 HCs who were closely matched for age, sex, and weight. Seed-based FC analysis was performed to identify alterations in the sFC and dFC patterns of the V1 in HM patients and HCs. Associations between mean sFC and dFC signal values and clinical symptoms in distinct brain areas among HM patients were identified via correlation analysis. Static and dynamic changes in brain activity in HM patients were investigated by assessments of sFC and dFC via calculation of the total time series mean and sliding-window analysis. Results: In the left anterior cingulate gyrus (L-ACG)/left superior parietal gyrus (L-SPG) and left V1, sFC values were significantly greater in HM patients than in HCs. In the L-ACG and right V1, sFC values were also significantly greater in HM patients than in HCs [two-tailed, voxel-level P < 0.01, Gaussian random field (GRF) correction, cluster-level P < 0.05]. In the left calcarine cortex (L-CAL) and left V1, dFC values were significantly lower in HM patients than in HCs. In the right lingual gyrus (R-LING) and right V1, dFC values were also significantly lower in HM patients than in HCs (two-tailed, voxel-level P < 0.01, GRF correction, cluster-level P < 0.05). Conclusion: Patients with HM exhibited significantly disturbed FC between the V1 and various brain regions, including L-ACG, L-SPG, L-CAL, and R-LING. This disturbance suggests that patients with HM could exhibit impaired cognitive and emotional processing functions, top-down control of visual attention, and visual information processing functions. HM patients and HCs could be distinguished from each other with high accuracy using sFC and dFC variabilities. These findings may help to identify the neural mechanism of decreased visual performance in HM patients. [ABSTRACT FROM AUTHOR]

Recent studies have found that electroencephalographic (EEG) features from different frequency bands and different brain regions contribute differently to emotion recognition (ER) in virtual reality (VR) and two-dimensional (2D) scenes. Despite achievements in the task of ER in both VR and 2D environments, there has not been much prior effort to develop a unified ER model that can be applied to both VR and 2D environments. We propose a novel Frequency-Band-Spatial-based Attention Network, named FBSA-Net, for ER in VR and 2D scenes. Specifically, FBSA-Net adaptively captures the frequency-band-spatial relationship of EEG signals through cascade fusion of frequency-band attention (FBA) module and spatial attention (SA) module. The FBA module can automatically assign FBA weights to each band feature. The SA module then applies the ProbeParse attention mechanism to adaptively explore channel relationships both within and across brain regions, thus facilitating a comprehensive understanding of the global features of the EEG signals. The model's efficacy was validated through the VRSDEED dataset, created from emotion induction experiments using VR and 2D induction programs. Multiple comparative experiments have shown that FBSA-Net achieved state-of-the-art recognition results on VRSDEED datasets, achieving average recognition accuracies of 96.63% and 96.49% in VR and 2D scenes, respectively. The research results indicate that the significant contribution of FBSA-Net is not only reflected in the ability to provide ER using the same model in VR and 2D environments, but also in its excellent ability to distinguish between the emotional states in these two scenarios, providing a valuable reference for using the same model for ER. [ABSTRACT FROM AUTHOR]

Aging-related cognitive decline is emerging as a health concern during the aging process of the global population. Hahn and colleagues found that glial aging was particularly accelerated in white matter compared to cortical regions. Specialized neuronal populations showed region-specific changes in gene expression. Acute dietary restriction triggers a reprogramming of genes associated with the circadian clock in glial cells, whereas injections of young mouse plasma selectively reverse age-related expression patterns. The discovery of region-specific aging could enhance our understanding of the aging process and offer new possibilities for innovative treatment strategies and interventions for cognitive impairments related to aging. [ABSTRACT FROM AUTHOR]

• We constructed the sparse brain functional connection map, taking advantage of the complementary of the linear PC and the nonlinear SRC functional connection matrices. • We proposed an MSCN model for the schizophrenia classification and achieved the highest classification accuracy compared with state-of-the-art methods. • The lenticular nucleus, supplementary motor area, and thalamus were the most discriminating brain regions. There was negative correlation between FC involving the cerebellar and amygdala and negative symptoms. Functional connectivity is an important feature for resting-state functional magnetic resonance image (rs-fMRI). The sparse representation-based classification method has advantages in schizophrenia recognition, and reducing the redundancy of functional connectivity effectively is the difficult point in research. In this study, we adopted the Pearson and Spearman rank correlation to design an SRC-based sparse matrix and proposed a deep-learning Multiple Sparsely Connected Network (MSCN) to complete the schizophrenia classification and abnormalities reveal of brain regions and networks. Evaluation indexes were adopted to evaluate the performance of the proposed method, and the effectiveness and superiority were tested by the private and public data set. We obtained the best classification accuracy (ACC = 91.71 %, SPE = 94.99 %, SEN = 88.69 %, F1 = 91.82 %, AUC = 0.9184, Kappa = 0.8518, MCC = 0.8250, Jaccard = 0.8455) on the private data set, and we also obtained the competitive results on the public COBRE data set. We demonstrated the superiority of the MSCN model by comparing with state-of-the-art methods. The abnormalities of brain region functional connection were revealed as follows. There are significant differences in the lentiform nucleus, complementary motor area, and thalamus of schizophrenia patients. There were significant differences in functional connectivity between paracentral lobules and auxiliary motor areas. Functional connections between the cerebellum and several brain regions are also reduced abnormally. Functional connections involving the right superior cerebellar lobe and the left inferior cerebellar lobe and the right amygdala were negatively associated with negative symptoms. This method can achieve the better classification result and reveal the potential pathogenesis of SZ, and it has important clinical application value. [ABSTRACT FROM AUTHOR]

While astrocyte heterogeneity is an important feature of the healthy brain, less is understood about spatiotemporal heterogeneity of astrocytes in brain disease. Spinocerebellar ataxia type 1 (SCA1) is a progressive neurodegenerative disease caused by a CAG repeat expansion in the gene Ataxin1 (ATXN1). We characterized astrocytes across disease progression in the four clinically relevant brain regions, cerebellum, brainstem, hippocampus, and motor cortex, of Atxn1154Q/2Q mice, a knock-in mouse model of SCA1. We found brain region-specific changes in astrocyte density and GFAP expression and area, early in the disease and prior to neuronal loss. Expression of astrocytic core homeostatic genes was also altered in a brain region-specific manner and correlated with neuronal activity, indicating that astrocytes may compensate or exacerbate neuronal dysfunction. Late in disease, expression of astrocytic homeostatic genes was reduced in all four brain regions, indicating loss of astrocyte functions. We observed no obvious correlation between spatiotemporal changes in microglia and spatiotemporal astrocyte alterations, indicating a complex orchestration of glial phenotypes in disease. These results support spatiotemporal diversity of glial phenotypes as an important feature of the brain disease that may contribute to SCA1 pathogenesis in a brain region and disease stage-specific manner. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate whether brain volume changes occur in patients with chronic ankle instability (CAI) using voxel-based morphometry and assessing correlations with clinical tests. Structural magnetic resonance imaging data were prospectively acquired in 24 patients with CAI and 34 healthy controls. CAI symptoms and pain intensity were assessed using the Foot and Ankle Ability Measure (FAAM), Cumberland Ankle Instability Tool (CAIT), American Orthopedic Foot and Ankle Society (AOFAS) ankle-hindfoot score, and visual analog scale (VAS). The gray matter volume (GMV) of each voxel was compared between the two groups while controlling for age, sex, weight, and education level. Correlation analysis was performed to identify associations between abnormal GMV regions and the FAAM score, AOFAS score, VAS score, disease duration, and body mass index. Patients with CAI exhibited reduced GMV in the right precentral and postcentral areas, right parahippocampal area, left thalamus, left parahippocampal area, and left postcentral area compared to that of healthy controls. Furthermore, the right parahippocampal (r = 0.642, p = 0.001), left parahippocampal (r = 0.486, p = 0.016), and left postcentral areas (r = 0.521, p = 0.009) were positively correlated with disease duration. The left thalamus was positively correlated with the CAIT score and FAAM activities of daily living score (r = 0.463, p = 0.023 and r = 0.561, p = 0.004, respectively). A significant positive correlation was found between the local GMV of the right and left parahippocampal areas (r = 0.487, p = 0.016 and r = 0.763, p < 0.001, respectively) and the AOFAS score. Neural plasticity may occur in the precentral and postcentral areas, parahippocampal area, and thalamus in patients with CAI. The patterns of structural reorganization in patients with CAI may provide useful information on the neuropathological mechanisms of CAI. [ABSTRACT FROM AUTHOR]

Neurological dysfunctions commonly occur after mild or moderate traumatic brain injury (TBI). Although most TBI patients recover from such a dysfunction in a short period of time, some present with persistent neurological deficits. Stress is a potential factor that is involved in recovery from neurological dysfunction after TBI. However, there has been limited research on the effects and mechanisms of stress on neurological dysfunctions due to TBI. In this review, we first investigate the effects of TBI and stress on neurological dysfunctions and different brain regions, such as the prefrontal cortex, hippocampus, amygdala, and hypothalamus. We then explore the neurobiological links and mechanisms between stress and TBI. Finally, we summarize the findings related to stress biomarkers and probe the possible diagnostic and therapeutic significance of stress combined with mild or moderate TBI. [ABSTRACT FROM AUTHOR]

Background: Images of anatomical regions and neuron type distribution, as well as their related literature are valuable assets for neuroscience research. They are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big data. The knowledge acquired from image data generally echoes with the literature accumulated over the years. The knowledge within the literature can provide a comprehensive context for a deeper understanding of the image data. However, it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale corpus. Thus, neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale literature. Methods: A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText dataset. This model takes advantage of BioBERT and CRF to predict entity labels while training. Results: The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText dataset. The BioBERT-CRF model has achieved the best average precision, recall, and F1 score of 81.3%, 84.0%, and 82.6%, respectively. We used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience dictionaries. Conclusions: Our work shows that the BioBERT-CRF model can be well-suited for brain region entity extraction. The rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about. [ABSTRACT FROM AUTHOR]

Aim: To study functional brain abnormalities in patients with hypertensive retinopathy (HR) and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations (fALFFs) method., Methods: Twenty HR patients and 20 healthy controls (HCs) were respectively recruited. The age, gender, and educational background characteristics of the two groups were similar. After functional magnetic resonance imaging (fMRI) scanning, the subjects' spontaneous brain activity was evaluated with the fALFF method. Receiver operating characteristic (ROC) curve analysis was used to classify the data. Further, we used Pearson's correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR., Results: The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus (RO-MFG) and right lingual gyrus. In contrast, the values of fALFFs in the left middle temporal gyrus (MTG), left superior temporal pole (STP), left middle frontal gyrus (MFG), left superior marginal gyrus (SMG), left superior parietal lobule (SPL), and right supplementary motor area (SMA) were higher in the HR group. The results of a t -test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group ( P <0.001). The fALFF values of the left middle frontal gyrus in HR patients were positively correlated with anxiety scores ( r =0.9232; P <0.0001) and depression scores ( r =0.9682; P <0.0001)., Conclusion: fALFF values in multiple brain regions of HR patients are abnormal, suggesting that these brain regions in HR patients may be dysfunctional, which may help to reveal the pathophysiological mechanisms of HR., Competing Interests: Conflicts of Interest: Wang XL, None; Zheng XJ, None; Zhang LJ, None; Hu JY, None; Wei H, None; Ling Q, None; He LQ, None; Chen C, None; Wang YX, None; Chen X, None; Shao Y, None., (International Journal of Ophthalmology Press.)

Aim: Patients with high myopia (HM) reportedly exhibit changes in functional brain activity, but the mechanism underlying such changes is unclear. This study was conducted to observe differences in dynamic spontaneous brain activity between patients with HM and healthy controls (HCs) via dynamic regional homogeneity (dReHo) analysis. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 82 patients with HM and 59 HCs who were closely matched for age, sex, and weight. The dReHo approach was used to assess local dynamic activity in the human brain. The association between mean dReHo signal values and clinical symptoms in distinct brain areas in patients with HM was determined via correlation analysis. Results: In the left fusiform gyrus (L-FG), right inferior temporal gyrus (R-ITG), right Rolandic operculum (R-ROL), right postcentral gyrus (R-PoCG), and right precentral gyrus (R-PreCG), dReHo values were significantly greater in patients with HM than in HCs. Conclusion: Patients with HM have distinct functional changes in various brain regions that mainly include the L-FG, R-ITG, R-ROL, R-PoCG, and R-PreCG. These findings constitute important evidence for the roles of brain networks in the pathophysiological mechanisms of HM and may aid in the diagnosis of HM. [ABSTRACT FROM AUTHOR]

Because of the difficulty in collecting fresh brains of humans at different ages, it remains unknown how epigenetic regulation occurs in the primate brains during aging. In the present study, we examined the genomic distribution of 5hmC, an indicator of DNA methylation, in the brain regions of non-human primates (rhesus monkey) at the ages of 2 (juvenile), 8 (young adult), and 17 (old) years. We found that genomic 5hmC distribution was accumulated in the monkey brain as age increased and displayed unique patterns in the cerebellumand striatumin an age-dependentmanner. We also observed a correlation between differentially hydroxymethylated regions (DhMRs) and genes that contribute to brain region-related functions and diseases. Our studies revealed, for the first time, the brain-region and age-dependent 5hmC modifications in the non-human primate and the association of these 5hmC modifications with brain region-specific function and potentially aging-related brain diseases. [ABSTRACT FROM AUTHOR]

Emotion recognition from affective brain-computer interfaces (aBCI) has garnered a lot of attention in human-computer interactions. Electroencephalographic (EEG) signals collected and stored in one database have been mostly used due to their ability to detect brain activities in real time and their reliability. Nevertheless, large EEG individual differences occur amongst subjects making it impossible for models to share information across. New labeled data is collected and trained separately for new subjects which costs a lot of time. Also, during EEG data collection across databases, different stimulation is introduced to subjects. Audio-visual stimulation (AVS) is commonly used in studying the emotional responses of subjects. In this article, we propose a brain region aware domain adaptation (BRADA) algorithm to treat features from auditory and visual brain regions differently, which effectively tackle subject-to-subject variations and mitigate distribution mismatch across databases. BRADA is a new framework that works with the existing transfer learning method. We apply BRADA to both cross-subject and cross-database settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks. [ABSTRACT FROM AUTHOR]

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Transcriptome studies have revealed age-, disease-, and region-associated microglial phenotypes reflecting changes in microglial function during development, aging, central nervous system homeostasis, and pathology. The molecular mechanisms that contribute to these transcriptomic changes are largely unknown. The aim of this study was to characterize the DNA methylation landscape of human microglia and the factors that contribute to variations in the microglia methylome. We hypothesized that both age and brain region would have a large impact on DNA methylation in microglia. Microglia from postmortem brain tissue of four different brain regions of 22 donors, encompassing 1 patient with schizophrenia, 13 patients with mood disorder pathology, and 8 control subjects, were isolated and assayed using a genome-wide methylation array. We found that human microglial cells have a methylation profile distinct from bulk brain tissue and neurons, and age explained a considerable part of the variation. Additionally, we showed that interindividual factors had a much larger effect on the methylation landscape of microglia than brain region, which was also seen at the transcriptome level. In our exploratory analysis, we found various differentially methylated regions that were related to disease status (mood disorder vs. control). This included differentially methylated regions that are linked to gene expression in microglia, as well as to myeloid cell function or neuropsychiatric disorders. Although based on relatively small samples, these findings suggest that the methylation profile of microglia is responsive to interindividual variations and thereby plays an important role in the heterogeneity of microglia observed at the transcriptome level. [ABSTRACT FROM AUTHOR]

Emotion recognition from affective brain-computer interfaces (aBCI) has garnered a lot of attention in human-computer interactions. Electroencephalographic (EEG) signals collected and stored in one database have been mostly used due to their ability to detect brain activities in real time and their reliability. Nevertheless, large EEG individual differences occur amongst subjects making it impossible for models to share information across. New labeled data is collected and trained separately for new subjects which costs a lot of time. Also, during EEG data collection across databases, different stimulation is introduced to subjects. Audio-visual stimulation (AVS) is commonly used in studying the emotional responses of subjects. In this article, we propose a brain region aware domain adaptation (BRADA) algorithm to treat features from auditory and visual brain regions differently, which effectively tackle subject-to-subject variations and mitigate distribution mismatch across databases. BRADA is a new framework that works with the existing transfer learning method. We apply BRADA to both cross-subject and cross-database settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks.

Profiling of 5-hydroxymethylcytosine (5hmC) in the brain regions of rhesus monkey at different ages reveals accumulation and tissue-specific patterns of 5hmC with aging. Region-specific differentially hydroxymethylated regions (DhMRs) are involved in neuronal functions and signal transduction. These data suggest that 5hmC may be a key regulator of gene transcription in neurodevelopment and thus a potential candidate for the epigenetic clock. Importantly, non-human primates are the ideal animal models for investigation of human aging and diseases not only because they are more genetically similar to humans but also epigenetically. [ABSTRACT FROM AUTHOR]

The oscillation phase of electroencephalograms (EEGs) is associated with behavioral performance. Several studies have demonstrated this association for relatively fast oscillations (>1 Hz); a similar finding has also been reported for slower oscillations, showing that behavioral performance is correlated with the phase of infraslow activity (ISA, 0.01-0.1 Hz) of electroencephalography (EEG). However, the previous study only investigated ISA in a local brain region using a relatively simple task (somatosensory discrimination task), leaving it difficult to determine how the EEG ISA for various brain regions is associated with behavioral performance. In addition, it is not known whether the EEG ISA phase modulates more complex behavioral task performance. In the present study, we analyzed the ISA of whole-brain EEG of participants performing various behaviors while playing video games. We found that behavior was associated with the specific oscillation phase of EEG ISA when that behavior was independent of other behaviors. In addition, we found that the EEG ISA oscillation phases modulating the different behaviors varied across brain regions. Our results suggest that the EEG ISA for different brain regions modulates behavioral performance in different ways and such modulation of EEG ISA can be generalized to diverse behaviors. This study may deepen the understanding of how EEG ISA modulates behavior and increases the applicability of EEG ISA. [ABSTRACT FROM AUTHOR]

Glial cell line-derived neurotrophic factor (GDNF) plays a critical role in neuronal survival and function. GDNF has two major splice variants in the brain, α-pro-GDNF and β-pro-GDNF, and both isoforms have strong neuroprotective effects on dopamine neurons. However, the expression of the GDNF splice variants in dopaminergic neurons in the brain remains unclear. Therefore, in this study, we investigated the mRNA and protein expression of α- and β-pro-GDNF in the mouse brain by real-time quantitative polymerase chain reaction, using splice variant-specific primers, and western blot analysis. At the mRNA level, β-pro-GDNF expression was significantly greater than that of α-pro-GDNF in the mouse brain. In contrast, at the protein level, α-pro-GDNF expression was markedly greater than that of β-pro-GDNF. To clarify the mechanism underlying this inverse relationship in mRNA and protein expression levels of the GDNF splice variants, we analyzed the expression of sorting protein-related receptor with A-type repeats (SorLA) by real-time quantitative polymerase chain reaction. At the mRNA level, SorLA was positively associated with β-pro-GDNF expression, but not with α-pro-GDNF expression. This suggests that the differential expression of α- and β-pro-GDNF in the mouse brain is related to SorLA expression. As a sorting protein, SorLA could contribute to the inverse relationship among the mRNA and protein levels of the GDNF isoforms. This study was approved by the Animal Ethics Committee of Xuzhou Medical University, China on July 14, 2016.

The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (training) samples may lead to a negative influence because they are significant dissimilar with the target (test) samples. So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples. Furthermore, for an EEG sample, from the aspect of neuroscience, not all the brain regions of an EEG sample contain emotional information that can transferred to the test data effectively. Even some brain region data will make strong negative effect for learning the emotional classification model. Considering these two issues, in this paper, we propose a transferable attention neural network (TANN) for EEG emotion recognition, which learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively through local and global attention mechanism. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator. Extensive experiments on EEG emotion recognition demonstrate that the proposed TANN is superior to those state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The ubiquitin-proteasome system (UPS) plays crucial roles in numerous cellular functions. Dysfunction of the UPS shows certain correlations with the pathological changes in Alzheimer's disease (AD). This study aimed to explore the different impairments of the UPS in multiple brain regions and identify hub ubiquitin ligase (E3) genes in AD. The brain transcriptome, blood transcriptome and proteome data of AD were downloaded from a public database. The UPS genes were collected from the Ubiquitin and Ubiquitin-like Conjugation Database. The hub E3 genes were defined as the differentially expressed E3 genes shared by more than three brain regions. E3Miner and UbiBrowser were used to predict the substrate of hub E3. This study shows varied impairment of the UPS in different brain regions in AD. Furthermore, we identify seven hub E3 genes (CUL1, CUL3, EIF3I, NSMCE1, PAFAH1B1, RNF175, and UCHL1) that are downregulated in more than three brain regions. Three of these genes (CUL1, EIF3I, and NSMCE1) showed consistent low expression in blood. Most of these genes have been reported to promote AD, whereas the impact of RNF175 on AD is not yet reported. Further analysis revealed a potential regulatory mechanism by which hub E3 and its substrate genes may affect transcription functions and then exacerbate AD. This study identified seven hub E3 genes and their substrate genes affect transcription functions and then exacerbate AD. These findings may be helpful for the development of diagnostic biomarkers and therapeutic targets for AD. [ABSTRACT FROM AUTHOR]

Objective To investigate the expression pattern of major histocompatibility complex class Ⅰ molecule（MHC-Ⅰ） in different brain regions of mouse，and to improve the understanding for nonimmune function of MHC-Ⅰ. Methods Immunofluorescence staining was used for labeling MHC-Ⅰ with OX-18 primary antibody in wide-type adult mice. Images were acquired on LeicaTCS SP8 confocal microscope，and analyzed by FIJI software. Results MHC-Ⅰ was widely distributed in various brain regions and had cell specificity.Even in the same nucleus，MHC-Ⅰ was unevenly distributed among different neurons in the brain. Somas of layer 2/3 and layer 5 neurons in the primary somatosensory cortex highly expressed MHC-Ⅰ.Both the hippocampal granular cell layer and the pyramidal cell layer had low levels of MHC-Ⅰ expression，and many scattered neurons expressed high level of MHC-Ⅰ .The high expression of MHC-Ⅰ in the amygdala was specifically concentrated in the basolateral amygdala nucleus，anterior part， while only a few neurons had low-level expression in the lateral amygdalar nucleus and basolateral amygdalar nucleus，posterior part.MHC-Ⅰ-positive neurons in the midbrain and thalamus were clustered and distributed in some specific nuclei. Conclusion MHC-Ⅰ is widely distributed in the mouse brain with cell specificity，which suggests its important role in central nerve system. [ABSTRACT FROM AUTHOR]

15N/ 13C values in the brain regions steadily increased according to postnatal development. The values in specific brain regions were significantly higher in rats with active motor performance, with high intelligence and subjected to long term maze training. Such differences were not found in the other tissues examined. • Ratios of naturally retained 15N to 13C (15N/13C) in rat tissues were measured. • 15N/13C in rat brain regions increase with postnatal development. • 15N /13C in hippocampus, cerebrum, and striatum correlate with motor activity. • 15N/13C in hippocampus and striatum are higher in rats with higher intelligence. • 15N/13C in brain regions of trained rats were higher than those in untrained rats. Our aim in the present study was to clarify the activity-dependent and function-associated retention of stable isotopes (SIs) in rat brain regions. We measured regional distributions of the natural stable isotopes 15N and 13C in brain using a mass spectrometer with a dual inlet system and a double collector for ratiometry, and compared them with distributions obtained from internal organs and skeletal muscle. Although levels of 15N and 13C were very high in brain regions of prenatal rats, and robustly decreased after birth, developmental changes in brain regions became obvious when the ratio of 15N to 13C (abbreviated as 15N/13C) in each brain region was compared. A high correlation was observed between free motor activity and 15N/13C in the hippocampus, cerebrum, and striatum. A significantly higher 15N/13C was also observed in the hippocampus and striatum of rats with higher intelligence, which was evaluated by radial maze learning. Furthermore, 15N/13C in brain regions of trained rats were significantly higher than those of untrained age-matched rats. Our study suggests that the 15N/13C in a specific brain region may reflect the physiological feature of the region. This ratio may hence be applicable as a maker for pathological research on undiagnosed brain diseases. [ABSTRACT FROM AUTHOR]

Diabetes at advanced age increases rise of cognitive impairment, but its potential mechanisms are still far from being fully understood. In this study, we analyzed the metabolic alterations in six different brain regions between streptozotocin (STZ)-induced diabetic mice with cognitive decline (DM) and age-matched controls (CON) using a 1H NMR-based metabolomics approach, to explore potential metabolic mechanisms underlying diabetes-induced cognitive decline. The results show that DM mice had a peculiar metabolic phenotype in all brain regions, mainly involving increased lactate level, decreased choline and energy metabolism as well as disrupted astrocyte-neuron metabolism. Furthermore, these metabolic changes exhibited a brain region-specific pattern. Collectively, our results suggest that brain region-specific metabolic disorders may be responsible for diabetes-induced cognitive dysfunction. [ABSTRACT FROM AUTHOR]