Comparing spatial distributions of ALA-PpIX and indocyanine green in a whole pig brain glioma model using 3D fluorescence cryotomography.

Significance: ALA-PpIX and second-window indocyanine green (ICG) have been studied widely for guiding the resection of high-grade gliomas. These agents have different mechanisms of action and uptake characteristics, which can affect their performance as surgical guidance agents. Elucidating these differences in animal models that approach the size and anatomy of the human brain would help guide the use of these agents. Herein, we report on the use of a new pig glioma model and fluorescence cryotomography to evaluate the 3D distributions of both agents throughout the whole brain. Aim: We aim to assess and compare the 3D spatial distributions of ALA-PpIX and second-window ICG in a glioma-bearing pig brain using fluorescence cryotomography. Approach: A glioma was induced in the brain of a transgenic Oncopig via adeno-associated virus delivery of Cre-recombinase plasmids. After tumor induction, the pro-drug 5-ALA and ICG were administered to the animal 3 and 24 h prior to brain harvest, respectively. The harvested brain was imaged using fluorescence cryotomography. The fluorescence distributions of both agents were evaluated in 3D in the whole brain using various spatial distribution and contrast performance metrics. Results: Significant differences in the spatial distributions of both agents were observed. Indocyanine green accumulated within the tumor core, whereas ALA-PpIX appeared more toward the tumor periphery. Both ALA-PpIX and second-window ICG provided elevated tumor-to-background contrast (13 and 23, respectively). Conclusions: This study is the first to demonstrate the use of a new glioma model and large-specimen fluorescence cryotomography to evaluate and compare imaging agent distribution at high resolution in 3D.

Application of electroencephalogram (EEG) in the study of the influence of different contents of alcohol and Baijiu on brain perception.

Alcoholic beverages flavour is complex and unique with different alcohol content, and the application of flavour perception could improve the objectivity of flavour evaluation. This study utilized electroencephalogram (EEG) to assess brain reactions to alcohol percentages (5 %-53 %) and Baijiu's complex flavours. The findings demonstrate the brain's proficiency in discerning between alcohol concentrations, evidenced by increasing physiological signal strength in tandem with alcohol content. When contrasted with alcohol solutions of equivalent concentrations, Baijiu prompts a more significant activation of brain signals, underscoring EEG's capability to detect subtleties due to flavour complexity. Additionally, the study reveals notable correlations, with δ and α wave intensities escalating in response to alcohol stimulation, coupled with substantial activation in the frontal, parietal, and right temporal regions. These insights verify the efficacy of EEG in charting the brain's engagement with alcoholic flavours, setting the stage for more detailed exploration into the neural encoding of these sensory experiences.

Dietary sn-2 palmitate influences cognitive behavior by increasing the transport of liver-produced lysophosphatidylcholine VLCPUFAs to the brain.

Investigations indicated that sn-2 palmitate have positive effects on brain development, although its mechanism remains largely unexamined. This research delved into how a diet abundant in sn-2 palmitate influenced the cognitive behavior of mice and elucidated the associated mechanisms using metabolomics and lipidomics. The study demonstrated that dietary sn-2 palmitate led to improved working memory and cognition in mice, as well as an increase in brain BDNF concentration when compared to those fed blend vegetable oil (BVO). This was because sn-2 palmitate feeding promoted the synthesis of very long-chain fatty acids (VLCPUFAs) for the lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) in the liver. This led to more efficient delivery of VLCPUFAs to the brain, as indicated by elevated concentration of LPC/LPE-VLCPUFAs in the liver and heightened expression of the major facilitator superfamily domain containing 2a (MFSD2A). In essence, this paper offered a potential mechanism by which sn-2 palmitate enhanced mouse neurodevelopment.

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.

Dual stage MRI image restoration based on blind spot denoising and hybrid attention.

BACKGROUND: Magnetic Resonance Imaging (MRI) is extensively utilized in clinical diagnostics and medical research, yet the imaging process is often compromised by noise interference. This noise arises from various sources, leading to a reduction in image quality and subsequently hindering the accurate interpretation of image details by clinicians. Traditional denoising methods typically assume that noise follows a Gaussian distribution, thereby neglecting the more complex noise types present in MRI images, such as Rician noise. As a result, denoising remains a challenging and practical task. METHOD: The main research work of this paper focuses on modifying mask information based on a global mask mapper. The mask mapper samples all blind spot pixels on the denoised image and maps them to the same channel. By incorporating perceptual loss, it utilizes all available information to improve performance while avoiding identity mapping. During the denoising process, the model may mistakenly remove some useful information as noise, resulting in a loss of detail in the denoised image. To address this issue, we train a generative adversarial network (GAN) with adaptive hybrid attention to restore the detailed information in the denoised MRI images. RESULT: The two-stage model NRAE shows an improvement of nearly 1.4 dB in PSNR and approximately 0.1 in SSIM on clinical datasets compared to other classic models. Specifically, compared to the baseline model, PSNR is increased by about 0.6 dB, and SSIM is only 0.015 lower. From a visual perspective, NRAE more effectively restores the details in the images, resulting in richer and clearer representation of image details. CONCLUSION: We have developed a deep learning-based two-stage model to address noise issues in medical MRI images. This method not only successfully reduces noise signals but also effectively restores anatomical details. The current results indicate that this is a promising approach. In future work, we plan to replace the current denoising network with more advanced models to further enhance performance.

Akkermansia muciniphila improve cognitive dysfunction by regulating BDNF and serotonin pathway in gut-liver-brain axis.

BACKRGROUND: Akkermansia muciniphila, a next-generation probiotic, is known as a cornerstone regulating the gut-organ axis in various diseases, but the underlying mechanism remains poorly understood. Here, we revealed the neuronal and antifibrotic effects of A. muciniphila on the gut-liver-brain axis in liver injury. RESULTS: To investigate neurologic dysfunction and characteristic gut microbiotas, we performed a cirrhosis cohort (154 patients with or without hepatic encephalopathy) and a community cognition cohort (80 participants in one region for three years) and validated the existence of cognitive impairment in a 3,5-diethoxycarbonyl-1,4-dihydrocollidine-induced hepatic injury mouse model. The effects of the candidate strain on cognition were evaluated in animal models of liver injury. The expression of brain-derived neurotrophic factor (BDNF) and serotonin receptors was accessed in patients with fibrosis (100 patients) according to the fibrosis grade and hepatic venous pressure gradient. The proportion of A. muciniphila decreased in populations with hepatic encephalopathy and cognitive dysfunction. Tissue staining techniques confirmed gut-liver-brain damage in liver injury, with drastic expression of BDNF and serotonin in the gut and brain. The administration of A. muciniphila significantly reduced tissue damage and improved cognitive dysfunction and the expression of BDNF and serotonin. Isolated vagus nerve staining showed a recovery of serotonin expression without affecting the dopamine pathway. Conversely, in liver tissue, the inhibition of injury through the suppression of serotonin receptor (5-hydroxytryptamine 2A and 2B) expression was confirmed. The severity of liver injury was correlated with the abundance of serotonin, BDNF, and A. muciniphila. CONCLUSIONS: A. muciniphila, a next-generation probiotic, is a therapeutic candidate for alleviating the symptoms of liver fibrosis and cognitive impairment.

Functional interactions underlying visuospatial orthographic processes in Chinese reading.

As a logographic writing system, Chinese reading involves the processing of visuospatial orthographic (ORT) properties. However, this aspect has received relatively less attention in neuroimaging research, which has tended to emphasize phonological (PHO) and semantic (SEM) aspects in processing Chinese characters. Here, we compared the functional correlates supporting all these three processes in a functional MRI single-character reading study, in which 35 native Chinese adults were asked to make ORT, PHO, and SEM judgments in separate task-specific activation blocks. Our findings revealed increased involvement of the right hemisphere in processing Chinese visuospatial orthography, particularly evident in the right ventral occipito-temporal cortex (vOTC). Additionally, time course analysis revealed that the left superior parietal gyrus (SPG) was initially involved in SEM processing but contributed to the visuospatial processing of words in a later time window. Finally, ORT processing demonstrated stronger recruitment of left vOTC-SPG-middle frontal gyrus (MFG) functional connectivity compared to SEM processing. This functional coupling correlated with reduced regional engagement of the left vOTC and MFG, highlighting that visuospatial ORT processes in reading Chinese rely on functional interactions among key regions rather than local regional processes. In conclusion, these findings underscore visuospatial ORT processes as a distinctive feature of reading logographic characters.

Symbolic information approach applied to human intracranial data to characterize and distinguish different congnitive processes.

How the human brain processes information during different cognitive tasks is one of the greatest questions in contemporary neuroscience. Understanding the statistical properties of brain signals during specific activities is one promising way to address this question. Here we analyze freely available data from implanted electrocorticography (ECoG) in five human subjects during two different cognitive tasks in the light of information theory quantifiers ideas. We employ a symbolic information approach to determine the probability distribution function associated with the time series from different cortical areas. Then we utilize these probabilities to calculate the associated Shannon entropy and a statistical complexity measure based on the disequilibrium between the actual time series and one with a uniform probability distribution function. We show that an Euclidian distance in the complexity-entropy plane and an asymmetry index for complexity are useful for comparing the two conditions. We show that our method can distinguish visual search epochs from blank screen intervals in different electrodes and patients. By using a multiscale approach and embedding time delays to downsample the data, we find important timescales in which the relevant information is being processed. We also determine cortical regions and time intervals along the 2-s-long trials that present more pronounced differences between the two cognitive tasks. Finally, we show that the method is useful to distinguish cognitive processes using brain activity on a trial-by-trial basis.

AB038. Establishing a reliable semi-automated segmentation method for assessing chemoradiotherapy effects on the non-tumoral brain.

BACKGROUND: Current voxel-based morphometry (VBM) studies of chemoradiotherapy effects on healthy tissues of the glioblastoma multiforme (GBM) brain face a challenge with neuroanatomical distortions (tumor, tumor edema, and resection cavities) and limited comparisons can be drawn across studies due to lack of a universally accepted software package. Our aim is to compare current semi-automated segmentation methods and optimize them for reliability in investigating the effects of chemoradiotherapy on GBM patients. METHODS: A publicly available dataset was used based on predefined inclusion and exclusion criteria. VBM pipelines CAT12 and FSL were tested and optimized to reduce the impact of neuroanatomical distortions. T1-weighted images were screened, and post-processed with FSL and CAT12. Gray matter (GM) and white matter (WM), and cerebrospinal fluid (CSF) volumes of whole brain, tumour-containing and non-tumor containing hemispheres, pre- and post-chemoradiotherapy were calculated and analyzed with Wilcoxon signed-rank tests. Agreement and consistency between FSL and CAT12 were assessed using Bland-Altman plots and intraclass correlation coefficients (ICCs). RESULTS: Post-chemoradiotherapy GM volumes were significantly reduced in whole brain with a compensatory significant increase in CSF volumes, while WM volumes had no significant changes. Similar trends were noted in tumor-containing and non-tumor-containing hemispheres. Bland-Altman plots showed good agreement between FSL and CAT12 processed GM and WM volumes of whole brain, tumor-containing, and non-tumor-containing hemispheres. ICC ≥0.70 was observed in GM [0.70 (0.53-0.82)] and WM [0.75 (0.60-0.85)] volumes of non-tumor-containing hemisphere, and WM [0.71 (0.55-0.83)] volumes of whole brain. GM volumes of tumor-containing hemisphere had good agreement but surprisingly, poor consistency [0.50 (0.25-0.68)]. CSF volumes in non-tumor-containing hemisphere had better agreement and consistency [0.55 (0.32-0.71)] than whole brain [0.49 (0.25-0.67)] and tumor-containing hemisphere CSF [0.36 (0.10-0.58)] volumes. Visual inspection revealed both CAT12 and FSL mis-segmented in the presence of neuroanatomical distortion although CAT12 was more susceptible in the presence of a hematoma. CONCLUSIONS: VBM studies of chemoradiotherapy effects on the brain post-tumor resection remain challenging due to neuroanatomical distortions. A reliable alternative is to use non-tumor-containing hemispheres with no anatomical distortion. Should tumor-containing brains be used, FSL is a more suitable choice, especially in the presence of hematoma distortion.

Neural responses to decision-making in suicide attempters with youth major depressive disorder.

An improved understanding of the factors associated with suicidal attempts in youth suffering from depression is crucial for the identification and prevention of future suicide risk. However, there is limited understanding of how neural activity is modified during the process of decision-making. Our study aimed to investigate the neural responses in suicide attempters with major depressive disorder (MDD) during decision-making. Electroencephalography (EEG) was recorded from 79 individuals aged 16-25 with MDD, including 39 with past suicide attempts (SA group) and 40 without (NSA group), as well as from 40 age- and sex- matched healthy controls (HCs) during the Iowa Gambling Task (IGT). All participants completed diagnostic interviews, self-report questionnaires. Our study examined feedback processing by measuring the feedback-related negativity (FRN), ΔFN (FRN-loss minus FRN-gain), and the P300 as electrophysiological indicators of feedback evaluation. The SA group showed poorest IGT performance. SA group and NSA group, compared with HC group, exhibited specific deficits in decision-making (i.e., exhibited smaller (i.e., blunted) ΔFN). Post hoc analysis found that the SA group was the least sensitive to gains and the most sensitive to losses. In addition, we also found that the larger the value of ΔFN, the better the decision-making ability and the lower the impulsivity. Our study highlights the link between suicide attempts and impaired decision-making in individuals with major depressive disorder. These findings constitute an important step in gaining a better understanding of the specific reward-related abnormalities that could contribute to the young MDD patients with suicide attempts.

Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging.

Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework.

The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

Temporal neural dynamics of understanding communicative intentions from speech prosody.

Understanding the correct intention of a speaker is critical for social interaction. Speech prosody is an important source for understanding speakers' intentions during verbal communication. However, the neural dynamics by which the human brain translates the prosodic cues into a mental representation of communicative intentions in real time remains unclear. Here, we recorded EEG (electroencephalograph) while participants listened to dialogues. The prosodic features of the critical words at the end of sentences were manipulated to signal either suggestion, warning, or neutral intentions. The results showed that suggestion and warning intentions evoked enhanced late positive event-related potentials (ERPs) compared to the neutral condition. Linear mixed-effects model (LMEM) regression and representational similarity analysis (RSA) analyses revealed that these ERP effects were distinctively correlated with prosodic acoustic analysis, emotional valence evaluation, and intention interpretation in different time windows; The onset latency significantly increased as the processing level of abstractness and communicative intentionality increased. Neural representations of intention and emotional information emerged and parallelly persisted over a long time window, guiding the correct identification of communicative intention. These results provide new insights into understanding the structural components of intention processing and their temporal neural dynamics underlying communicative intention comprehension from speech prosody in online social interactions.

The efficient method to get better raw brain signal on rat anesthetics experiment.

This paper introduces an efficient methodology for conducting rat anesthesia experiments, aimed at enhancing the quality of raw brain signals obtained. The proposed approach enables the acquisition of animal brain signals during experiments without the confounding influence of muscle noise. Initially, the use of alpha-chloralose (a-c) in conjunction with Isoflurane is introduced to induce anesthesia in rats. Subsequently, Dexdomitor is administered to prevent muscular movements during the collection of brain signals, further refining the signal quality. Experimental outcomes conclusively demonstrate that our anesthesia method produces cleaner raw signals and exhibits improved robustness during data acquisition, outperforming existing methods that rely solely on Isoflurane or the Ketamine-Xylazine combination. Notably, this improved performance is achieved with minimal alterations to vital physiological parameters, including body temperature, respiration, and heart rates. Moreover, the efficacy of a-c in maintaining anesthesia for up to 7 h stands in contrast to the shorter durations achievable with continuous Isoflurane administration or the 30-min window offered by Ketamine-Xylazine, highlighting the practical advantages of our proposed method. Finally, post-experiment observations confirmed that the animals gradually returned to normal behavior without any signs of distress or adverse effects, indicating that our method was both effective and safe.

A comparison of EEG encoding models using audiovisual stimuli and their unimodal counterparts.

Communication in the real world is inherently multimodal. When having a conversation, typically sighted and hearing people use both auditory and visual cues to understand one another. For example, objects may make sounds as they move in space, or we may use the movement of a person's mouth to better understand what they are saying in a noisy environment. Still, many neuroscience experiments rely on unimodal stimuli to understand encoding of sensory features in the brain. The extent to which visual information may influence encoding of auditory information and vice versa in natural environments is thus unclear. Here, we addressed this question by recording scalp electroencephalography (EEG) in 11 subjects as they listened to and watched movie trailers in audiovisual (AV), visual (V) only, and audio (A) only conditions. We then fit linear encoding models that described the relationship between the brain responses and the acoustic, phonetic, and visual information in the stimuli. We also compared whether auditory and visual feature tuning was the same when stimuli were presented in the original AV format versus when visual or auditory information was removed. In these stimuli, visual and auditory information was relatively uncorrelated, and included spoken narration over a scene as well as animated or live-action characters talking with and without their face visible. For this stimulus, we found that auditory feature tuning was similar in the AV and A-only conditions, and similarly, tuning for visual information was similar when stimuli were presented with the audio present (AV) and when the audio was removed (V only). In a cross prediction analysis, we investigated whether models trained on AV data predicted responses to A or V only test data similarly to models trained on unimodal data. Overall, prediction performance using AV training and V test sets was similar to using V training and V test sets, suggesting that the auditory information has a relatively smaller effect on EEG. In contrast, prediction performance using AV training and A only test set was slightly worse than using matching A only training and A only test sets. This suggests the visual information has a stronger influence on EEG, though this makes no qualitative difference in the derived feature tuning. In effect, our results show that researchers may benefit from the richness of multimodal datasets, which can then be used to answer more than one research question.

Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia.

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.

Microbiota-gut-brain axis: Natural antidepressants molecular mechanism.

BACKGROUND: Major depressive disorder (MDD) is a severe mental health condition characterized by persistent depression, impaired cognition, and reduced activity. Increasing evidence suggests that gut microbiota (GM) imbalance is closely linked to the emergence and advancement of MDD, highlighting the potential significance of regulating the "Microbiota-Gut-Brain" (MGB) axis to impact the development of MDD. Natural products (NPs), characterized by broad biological activities, low toxicity, and multi-target characteristics, offer unique advantages in antidepressant treatment by regulating MGB axis. PURPOSE: This review was aimed to explore the intricate relationship between the GM and the brain, as well as host responses, and investigated the mechanisms underlying the MGB axis in MDD development. It also explored the pharmacological mechanisms by which NPs modulate MGB axis to exert antidepressant effects and addressed current research limitations. Additionally, it proposed new strategies for future preclinical and clinical applications in the MDD domain. METHODS: To study the effects and mechanism by which NPs exert antidepressant effects through mediating the MGB axis, data were collected from Web of Science, PubMed, ScienceDirect from initial establishment to March 2024. NPs were classified and summarized by their mechanisms of action. RESULTS: NPs, such as flavonoids,alkaloids,polysaccharides,saponins, terpenoids, can treat MDD by regulating the MGB axis. Its mechanism includes balancing GM, regulating metabolites and neurotransmitters such as SCAFs, 5-HT, BDNF, inhibiting neuroinflammation, improving neural plasticity, and increasing neurogenesis. CONCLUSIONS: NPs display good antidepressant effects, and have potential value for clinical application in the prevention and treatment of MDD by regulating the MGB axis. However, in-depth study of the mechanisms by which antidepressant medications affect MGB axis will also require considerable effort in clinical and preclinical research, which is essential for the development of effective antidepressant treatments.

The association between social rewards and anxiety: Links from neurophysiological analysis in virtual reality and social interaction game.

Individuals' affective experience can be intricate, influenced by various factors including monetary rewards and social factors during social interaction. However, within this array of factors, divergent evidence has been considered as potential contributors to social anxiety. To gain a better understanding of the specific factors associated with anxiety during social interaction, we combined a social interaction task with neurophysiological recordings obtained through an anxiety-elicitation task conducted in a Virtual Reality (VR) environment. Employing inter-subject representational similarity analysis (ISRSA), we explored the potential linkage between individuals' anxiety neural patterns and their affective experiences during social interaction. Our findings suggest that, after controlling for other factors, the influence of the partner's emotional cues on individuals' affective experiences is specifically linked to their neural pattern of anxiety. This indicates that the emergence of anxiety during social interaction may be particularly associated with the emotional cues provided by the social partner, rather than individuals' own reward or prediction errors during social interaction. These results provide further support for the cognitive theory of social anxiety and extend the application of VR in future cognitive and affective studies.

α-Synuclein strain propagation is independent of cellular prion protein expression in a transgenic synucleinopathy mouse model.

The cellular prion protein, PrPC, has been postulated to function as a receptor for α-synuclein, potentially facilitating cell-to-cell spreading and/or toxicity of α-synuclein aggregates in neurodegenerative disorders such as Parkinson's disease. Previously, we generated the "Salt (S)" and "No Salt (NS)" strains of α-synuclein aggregates that cause distinct pathological phenotypes in M83 transgenic mice overexpressing A53T-mutant human α-synuclein. To test the hypothesis that PrPC facilitates the propagation of α-synuclein aggregates, we produced M83 mice that either express or do not express PrPC. Following intracerebral inoculation with the S or NS strain, the absence of PrPC in M83 mice did not prevent disease development and had minimal influence on α-synuclein strain-specified attributes such as the extent of cerebral α-synuclein deposition, selective targeting of specific brain regions and cell types, the morphology of induced α-synuclein deposits, and the structural fingerprints of protease-resistant α-synuclein aggregates. Likewise, there were no appreciable differences in disease manifestation between PrPC-expressing and PrPC-lacking M83 mice following intraperitoneal inoculation of the S strain. Interestingly, intraperitoneal inoculation with the NS strain resulted in two distinct disease phenotypes, indicative of α-synuclein strain evolution, but this was also independent of PrPC expression. Overall, these results suggest that PrPC plays at most a minor role in the propagation, neuroinvasion, and evolution of α-synuclein strains in mice that express A53T-mutant human α-synuclein. Thus, other putative receptors or cell-to-cell propagation mechanisms may have a larger effect on the spread of α-synuclein aggregates during disease.