Neuroscience
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Bayesian brain theory, a computational framework grounded in the principles of Predictive Processing (PP), proposes a mechanistic account of how beliefs are formed and updated. This theory assumes that the brain encodes a generative model of its environment, made up of probabilistic beliefs organized in networks, from which it generates predictions about future sensory inputs. The difference between predictions and sensory signals produces prediction errors, which are used to update belief networks. In this article, we introduce the fundamental principles of Bayesian brain theory, and show how the brain dynamics of prediction are associated with the generation and evolution of beliefs.
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Focal Cortical Dysplasia (FCD) & Mesial Temporal Lobe Epilepsy-Hippocampal Sclerosis (MTLE-HS) are two common pathologies of drug-resistant focal epilepsy (DRE). Inappropriate localization of the epileptogenic zones (EZs) in FCD is a significant contributing factor to the unsatisfactory surgical results observed in FCD cases. Currently, no molecular or cellular indicators are available which can aid in identifying the epileptogenic zones (EZs) in FCD. ⋯ These findings suggest that employing distinct lipid mass spectra could be an effective method for identifying the EZs in FCD. The unique lipid mass spectra of cortical tissues from patients with FCD can be utilized for real-time surgical guidance. Additionally, the plasma triglyceride (TAG) level has the potential to act as a biomarker once validated on a larger cohort.
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The association of neuroticism and cerebral small vessel disease (CSVD) development remains unclear. In this study, we used Mendelian randomization (MR) to explore the potential role of neuroticism in CSVD development. ⋯ This research suggests a potential correlation between certain aspects of neuroticism and CSVD, with further studies needed to better understand the causal relationship and its implications for patient intervention.
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The optimal stimulation frequency for inducing neuromodulatory effects remains unclear. The purpose of our study was to investigate the effect of neuromuscular electrical stimulation (NMES) with different frequencies on cortical and spinal excitability. Thirteen able-bodied individuals participated in the experiment involving NMES: (i) low-frequency at 25 Hz, (ii) high-frequency at 100 Hz, and (iii) mixed-frequency at 25 and 100 Hz switched every one second. ⋯ Our results showed that mixed frequency was most effective in modulating corticospinal excitability, although motor performance was not affected by any intervention. The cortical silent period was prolonged and Mmax was inhibited by all frequencies, while the F-wave and MVC were unaffected. Mixed-frequency stimulation could recruit a more diverse range of motor units, which are recruited in a stimulus frequency-specific manner, than single-frequency stimulation, and thus may have affected corticospinal facilitation.
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The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. Resting-state functional magnetic resonance imaging (Rs-fMRI) can reflect the fluctuation data of brain activity in a resting state, which can find the interrelationships, functional connections, and network characteristics among brain regions of the patients. In this paper, a brain functional connectivity matrix is constructed using Pearson correlation based on the characteristics of multi-site Rs-fMRI data and brain atlas, and an adaptive propagation operator graph convolutional network (APO-GCN) model is designed. ⋯ The experimental results on Rs-fMRI data from 1601 participants (830 MDD and 771 HC) and 16 sites of REST-meta-MDD project show that the APO-GCN achieved a classification accuracy of 91.8%, outperforming those of the state-of-the-art classifier methods. The classification process is driven by multiple significant brain regions, and our method further reveals functional connectivity abnormalities between these brain regions, which are important biomarkers of classification. It is worth noting that the brain regions identified by the classifier and the networks involved are consistent with existing research results, which suggest that the pathogenesis of depression may be related to dysfunction of multiple brain networks.