Frontiers in Neuroscience最新文献

Autism spectrum disorder (ASD) is characterized by highly heterogenous abnormalities in functional brain connectivity affecting social behavior. There is a significant progress in understanding the molecular and genetic basis of ASD in the last decade using multi-omics approach. Mining this large volume of biomedical literature for insights requires considerable amount of manual intervention for curation. Machine learning and artificial intelligence fields are advancing toward simplifying data mining from unstructured text data. Here, we demonstrate our literature mining pipeline to accelerate data to insights. Using topic modeling and generative AI techniques, we present a pipeline that can classify scientific literature into thematic clusters and can help in a wide array of applications such as knowledgebase creation, conversational virtual assistant, and summarization. Employing our pipeline, we explored the ASD literature, specifically around multi-omics studies to understand the molecular interplay underlying autism brain.

Holistic processing is commonly measured by the face inversion effect (FIE) and the composite face effect (CFE). Previous studies examining whether individuals with autism spectrum disorder (ASD) employ holistic processing using either FIE or CFE have reported inconclusive results. By adopting a customized composite face paradigm, the present study aims to simultaneously assess both the inversion and the composite effects of holistic processing in autistic and neurotypical adults. We tested 24 adults with ASD and 24 neurotypical (NT) adults matched in age, gender, and years of education. Participants viewed sequentially presented composite faces in three Presentation Modes (aligned, inverted, and misaligned) with three Stimuli Conditions (same, composite, and different) and judged whether the top half was the same. For the dependent variables, we calculated a "performance index" in the form of the accuracy/response time of each stimuli condition in each presentation mode. The FIE and CFE were computed to index the magnitude of holistic processing. Our results showed that the NT group responded more accurately in less time than the ASD group across task conditions. Notably, both the NT and the ASD groups exhibited a significant FIE with similar magnitude. Likewise, both the NT and the ASD groups showed a greater-than-zero CFE. Moreover, individuals' CFE positively correlated with FIE and negatively correlated with the AQ scores for all participants. In summary, individuals with ASD exhibit holistic processing when viewing faces, evidenced by the presence of both FIE and CFE and the positive correlations between the two effects.

Introduction: Inspiratory muscle fatigue has been shown to have effects on the autonomic nervous system and physical condition. This study aimed to evaluate the influence of an inspiratory muscle fatigue protocol on respiratory muscle strength and heart rate variability in older adults.

Materials and methods: A randomized controlled clinical trial with double-blinding was carried out involving 24 individuals over 60 years old who demonstrated physical independence in walking and movement. Participants were distributed randomly into three groups: Inspiratory muscle fatigue, activation and control. Measurements of heart rate variability, diaphragmatic ultrasound, and maximum inspiratory pressure were taken at two stages: prior to the intervention (T1) and directly after treatment (T2).

Results: The inspiratory muscle fatigue group exhibited decrease scores in respiratory and heart rate variability subsequent to undergoing the diaphragmatic fatigue intervention compared to both the activation and control groups (p < 0.05). Conversely, the activation group demonstrated higher values in heart rate variability and respiratory capacity variables following the inspiratory muscle activation training (p < 0.05).

Conclusions: Fatigue of the inspiratory musculature appears to negatively impact heart rate variability and inspiratory muscle strength in older adults.

Clinical trial registration: https://clinicaltrials.gov/study/NCT06269042, identifier: NCT06269042.

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks.

Introduction: Right-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.

Methods: Firstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.

Results: Our experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.

Discussion: The results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

Background: Alzheimer's disease (AD), characterized by cognitive impairment and depression, is currently one of the intractable problems due to the insufficiency of intervention strategies. Diethyl butylmalonate (DBM) has recently attracted extensive interest due to its anti-inflammatory role in macrophages. However, it is still unknown whether DBM has a beneficial effect on cognitive deficits and depression.

Methods: DBM was administrated to 5×FAD and C57BL/6J mice by intraperitoneal injection. Novel object recognition, Y-maze spatial memory, Morris water maze and nest building tests were used to evaluate cognitive function. Moreover, the tail suspension test, forced swimming test, open field test and the elevated plus maze test were used to assess depression. Transmission electron microscopy, Golgi-Cox staining, immunofluorescence, RT-qPCR and western blot were utilized to determine the neuropathological changes in the hippocampus and amygdala of mice.

Results: Multiple behavioral tests showed that DBM effectively mitigated cognitive deficit and depression in 5×FAD mice. Moreover, DBM significantly attenuated synaptic ultrastructure and neurite impairment in the hippocampus of 5×FAD mice, paralleled by the improvement of the deficits of PSD95 and BDNF proteins. In addition, DBM decreased the accumulation of microglia and downregulated neuroinflammation in the hippocampus and amygdala of 5×FAD mice.

Conclusion: This study provides evidence that DBM ameliorates cognitive deficits and depression via improvement of the impairment of synaptic ultrastructure and neuroinflammation, suggesting that DBM is a potential drug candidate for treating AD-related neurodegeneration.

Recently, significant advancements have been made in the field of efficient single-image super-resolution, primarily driven by the innovative concept of information distillation. This method adeptly leverages multi-level features to facilitate high-resolution image reconstruction, allowing for enhanced detail and clarity. However, many existing approaches predominantly emphasize the enhancement of distilled features, often overlooking the critical aspect of improving the feature extraction capabilities of the distillation module itself. In this paper, we address this limitation by introducing an asymmetric large-kernel convolution design. By increasing the size of the convolution kernel, we expand the receptive field, which enables the model to more effectively capture long-range dependencies among image pixels. This enhancement significantly improves the model's perceptual ability, leading to more accurate reconstructions. To maintain a manageable level of model complexity, we adopt a lightweight architecture that employs asymmetric convolution techniques. Building on this foundation, we propose the Lightweight Asymmetric Large Kernel Distillation Network (ALKDNet). Comprehensive experiments conducted on five widely recognized benchmark datasets-Set5, Set14, BSD100, Urban100, and Manga109-indicate that ALKDNet not only preserves efficiency but also demonstrates performance enhancements relative to existing super-resolution methods. The average PSNR and SSIM values show improvements of 0.10 dB and 0.0013, respectively, thereby achieving state-of-the art performance.

Neurodegenerative diseases (NDs) are increasingly prevalent in our aging population, imposing significant social and economic burdens. Currently, most ND patients receive only symptomatic treatment due to limited understanding of their underlying causes. Consequently, there is a pressing need for comprehensive research into the pathological mechanisms of NDs by both researchers and clinicians. Autophagy, a cellular mechanism responsible for maintaining cellular equilibrium by removing dysfunctional organelles and misfolded proteins, plays a vital role in cell health and is implicated in various diseases. MicroRNAs (miRNAs) exert influence on autophagy and hold promise for treating these diseases. These small oligonucleotides bind to the 3'-untranslated region (UTR) of target mRNAs, leading to mRNA silencing, degradation, or translation blockade. This review explores recent findings on the regulation of autophagy and autophagy-related genes by different miRNAs in various pathological conditions, including neurodegeneration and inflammation-related diseases. The recognition of miRNAs as key regulators of autophagy in human diseases has spurred investigations into pharmacological compounds and traditional medicines targeting these miRNAs in disease models. This has catalyzed a new wave of therapeutic interventions aimed at modulating autophagy.

Analyzing the types of neurons based on morphological characteristics is pivotal for understanding brain function and human development. Existing analysis approaches based on 2D view images fully use complementary information across images. However, these methods ignore the redundant information caused by similar images and the effects of different views on the analysis results during the fusion process. Considering these factors, this paper proposes a Multi-gate Weighted Fusion Network (MWFNet) to characterize neuronal morphology in a hierarchical manner. MWFNet mainly consists of a Gated View Enhancement Module (GVEM) and a Gated View Measurement Module (GVMM). GVEM enhances view-level descriptors and eliminates redundant information by mining the relationships among different views. GVMM calculates the weights of view images based on the salient activated regions to assess their influence on the analysis results. Furthermore, the enhanced view-level features are fused differentially according to the view weight to generate a more discriminative instance-level descriptor. In this way, the proposed MWFNet not only eliminates unnecessary features but also maps the representation differences of views into decision-making. This can improve the accuracy and robustness of MWFNet for the identification of neuron type. Experimental results show that our method achieves accuracies of 91.73 and 98.18% on classifying 10 types and five types of neurons, respectively, outperforming other state-of-the-art methods.

Introduction: Considering the high economic burden and risks of deep brain stimulation (DBS) surgical failure, predicting the motor outcomes of DBS in Parkinson's disease (PD) is of significant importance in clinical decision-making. Functional controllability provides a rationale for combining the abnormal connections of the cortico-striato-thalamic-cortical (CSTC) motor loops and dynamic changes after medication in DBS outcome prediction.

Methods: In this study, we analyzed the association between preoperative delta functional controllability after medication within CSTC loops and motor outcomes of subthalamic nucleus DBS (STN-DBS) and globus pallidus interna DBS (GPi-DBS) and predicted motor outcomes in a Support Vector Regression (SVR) model using the delta controllability of focal regions.

Results: While the STN-DBS motor outcomes were associated with the delta functional controllability of the thalamus, the GPi-DBS motor outcomes were related to the delta functional controllability of the caudate nucleus and postcentral gyrus. In the SVR model, the predicted and actual motor outcomes were positively correlated, with p = 0.020 and R = 0.514 in the STN-DBS group, and p = 0.011 and R = 0.705 in the GPi- DBS group.

Discussion: Our findings indicate that different focal regions within the CSTC motor loops are involved in STN-DBS and GPi-DBS and support the feasibility of functional controllability in predicting DBS motor outcomes for PD in clinical decision-making.