Brain Research Bulletin最新文献

Background: Intense stress greatly affects athletes' mental state and performance, and cognitive control, which is vital for handling unexpected situations and maintaining peak performance. Although previous research has shown that acute stress can impact different aspects of cognitive control, the exact mechanisms and effects are still debated. This study aimed to investigate how acute stress impacts proactive and reactive control in table tennis players, providing new insights into the intricate role of stress in athletes' cognitive control.

Methods: Acute stress was induced in 25 male table tennis athletes using the Maastricht Acute Stress Task (MAST). Behavioral and electroencephalogram (EEG) data were recorded before and after acute stress exposure. The EEG data included both resting-state and task-related recordings. Functional connectivity analysis was conducted on the resting-state EEG data. Event-related potential (ERP) analysis was conducted on the task-related EEG data recorded during the AX-Continuous Performance Task (AX-CPT).

Results: The behavioral results showed similar reaction times in table tennis athletes before and after acute stress, with AY trials showing significantly higher accuracy. The ERP results revealed a decrease in contingent negative variation (CNV) amplitude and increased N2 component but no significant difference was observed between the P3b and P3a components before and after acute stress. Functional connectivity analysis showed that acute stress activated the visual network and enhanced functional connectivity with multiple brain regions.

Conclusion: Acute stress induces large-scale activation of visual and salience networks while affecting the two modes of cognitive control, with reactive control predominating over proactive control. The weakening of proactive control by acute stress affected response preparation rather than pre-attention and stimuli processing, while the enhancement of reactive control resulted increased detection and monitoring of conflicting information. These findings shed light on how acute stress induces a neurocognitive shift in athletes, contributing to the advancement of the Dual Mechanisms of Cognitive Control (DMC) framework. This shift highlights the critical balance between proactive and reactive control under stress, offering valuable insights for developing interventions that optimize athletes' performance under high-pressure conditions.

Background: Glioblastoma (GBM), which is characterized by high invasiveness and spatial heterogeneity, poses significant challenges in terms of prognosis and treatment. In this study, whole-brain network topology was combined with gene coexpression analysis to identify potential imaging and molecular markers for glioma grading, shifting the research focus from localized tumor lesions to the interaction mechanisms between the nervous system and gliomas.

Methods: In this study, data from open-access datasets, including glioma imaging data from the Brain Tumor Segmentation Challenge (BraTS'2019), data on glioma-related genes from BrainBase, and whole-brain transcriptomic data from the Allen Human Brain Atlas, were used. Wavelet-based features were extracted separately from T1- and T2-weighted magnetic resonance (MR) images and integrated to construct individualized structural brain networks with the aim of characterizing glioma heterogeneity. LASSO regression was applied to perform feature selection and extract the representative structural network metrics. We evaluated the effectiveness of the derived network features in glioma grading from this novel perspective via four classification models: support vector machine (SVM), random forest, logistic regression, and XGBoost. Furthermore, weighted gene coexpression network analysis (WGCNA) was employed to link disruptions in structural connectivity between high- and low-grade gliomas with the expression patterns of glioma-related genes, thereby identifying potential molecular markers associated with tumor progression.

Results: The results demonstrated that the whole-brain network-based grading approach achieved test-set AUC values above 0.85 across all four models, with the SVM and logistic regression models showing the best performance (AUC = 0.87). Additionally, the high invasiveness of high-grade gliomas may lead to the decoupling and reorganization of structural network modules. Protein kinase C beta (PRKCB) has been identified as a potential molecular biomarker associated with glioma grade.

Conclusions: Wavelet-based individualized brain structural networks have been proposed for glioma grading models, resulting in novel imaging and molecular markers for cancer neuroscience.

This study explored using deep learning (DL) to separate short-interval staggered ¹¹C-CFT/¹⁸F-FDG brain PET images for Parkinson's disease, aiming to reduce scan waiting time. A total of 67 patients performing ¹¹C-CFT and ¹⁸F-FDG brain PET scans on separate days were retrospectively enrolled. A Swin UNETR model was trained to generate pseudo ¹⁸F-FDG PET images from simulated dual-tracer sum images. The simulation assumed ¹⁸F-FDG was administered at 80, 100, 120, or 200 min (∆t) after ¹¹C-CFT injection. Compared to actual ¹⁸F-FDG images, the pseudo images showed high visual similarity across all ∆t intervals. Low average NMSE values (∼0.0004) and high average SSIM values (0.9991-0.9993) were consistently achieved across all Δt groups. Bland & Altman analysis of the whole brain region demonstrated low average SUVR bias across all Δt groups remained within ±0.001. Region-wise correlation analysis revealed strong correlations between actual and pseudo ¹⁸F-FDG images across all Δt, with slopes ranging from 0.994 to 1.001, and all R²> 0.99. SUVmean, LBR and SNR values for pseudo ¹⁸F-FDG images exhibited no statistically significant differences compared to actual ¹⁸F-FDG images (P > 0.05). Dual-tracer PET images can be effectively separated using the DL model, yielding high-quality visual and semi-quantitative results when ¹⁸F-FDG is injected immediately after the ¹¹C-CFT PET scan, thereby reducing patient wait time, improving patient comfort, and enhancing overall clinical efficiency.

Background: Post-stroke sleep disorders are frequent complications of ischemic stroke and contribute to poor neurological recovery. Dual orexin receptor antagonists (DORAs) promote sleep via mechanisms distinct from traditional hypnotics. This study investigated whether almorexant attenuates sleep deprivation (SD)-aggravated ischemic injury in rats and explored the its potential association with PI3K/Akt/mTOR signaling pathway.

Methods: Male Sprague-Dawley rats underwent permanent middle cerebral artery occlusion (MCAO), and were assigned to sham, MCAO, MCAO+SD, MCAO+SD+almorexant, or MCAO+SD+estazolam. Neurological function (Zea-Longa score, open-field test), infarct volume (TTC), histopathology (H&E), neuronal apoptosis (TUNEL), and PI3K/Akt/mTOR expression (Western blot, RT-qPCR) were assessed.

Results: SD exacerbated neurological deficits after MCAO, as reflected by higher Zea-Longa scores and impaired locomotor activity. Almorexant treatment was associated with improved behavioral performance and reduced histologic injury, neuronal apoptosis in sleep-deprived MCAO rats At the molecular level, both almorexant and estazolam treatments were associated with upregulated PI3K, Akt, and mTOR expression at both protein and mRNA levels.

Conclusion: Almorexant was associated with attenuation of SD-exacerbated ischemic brain injury in rats, accompanied by alterations in PI3K/Akt/mTOR signaling, suggesting DORAs may have potential relevance for the management of post-stroke insomnia.

Purpose: This study compared the time-dependent changes in brain metabolites and gut microbiota at early and late post-irradiation stages in mice receiving conventional radiotherapy (CONV-RT) or ultra-high dose-rate FLASH radiotherapy (FLASH-RT).

Methods: Male C57BL/6 J mice received whole-brain irradiation using CONV-RT (2 Gy/min) or X-ray-based FLASH-RT (200 Gy/s). Brain tissue and gut contents were collected on days 1, 3, 7, and 21 post-irradiation. Targeted LC-MS metabolomics profiled dynamic brain metabolite changes, and 16S rRNA sequencing characterized gut microbiota trajectories. Neuroinflammation and brain injury were assessed by immunofluorescence (microglia/astrocyte markers) and laser speckle cerebral blood flow imaging, while behavioral assays evaluated cognition and anxiety-like behaviors.

Results: Compared with CONV-RT, FLASH-RT was associated with less pronounced hippocampal microglial and astrocytic reactivity, a smaller reduction in cerebral blood flow perfusion, and attenuated radiation-associated cognitive deficits. Metabolomics revealed distinct temporal trajectories: CONV-RT showed sustained late-phase suppression of metabolic programs, whereas FLASH-RT exhibited a coordinated late-stage rebound in pathways related to amino acid/carbohydrate metabolism and synaptic neurotransmission. Mechanistically relevant metabolites linked to antioxidation, energy metabolism, and neural repair were increased under FLASH-RT, consistent with reduced neuroinflammation. Gut microbiota profiling demonstrated that FLASH-RT induced a milder and more transient dysbiosis, with faster restoration toward a Sham-like structure and enrichment of putative beneficial taxa (including Lachnospiraceae, Peptostreptococcaceae, and Dubosiella), while CONV-RT was associated with more persistent Proteobacteria/Enterobacteriaceae-related disruptions and opportunistic signatures.

Conclusion: In summary, we have innovatively explored the changes in brain metabolites and gut microbiota induced by FLASH-RT whole-brain irradiation, providing a theoretical foundation for further investigation into the mechanisms underlying FLASH-RT's effects.