Brain Organoid and Systems Neuroscience Journal最新文献

Background

Material and method

Results

Conclusion

Stroke, the second leading cause of death, exhibits no significant sex differences and primarily affects the elderly, with sociodemographic and income factors playing a role. Lifestyle patterns, including elevated blood pressure, weight, glucose levels, air pollution exposure, smoking, and nutrition, contribute to stroke risk. Stroke's impact on the brain's functional and structural integrity leads to cognitive deficits and challenges in daily activities. Rehabilitation is crucial for functional recovery. This review explores the association between brain networks and behavioral deficits post-stroke, aiming to establish a cartographic approach for predicting rehabilitation outcomes. Methodologically, a systematic review following PRISMA-ScR guidelines was conducted, searching PUBMED and SCOPUS for relevant studies from 2003 to 2023. The synthesis of 29 studies reveals insights into language, comprehension, general cognition, praxis, and complex cognitive abilities after stroke. Language recovery involves networks like the presupplementary motor area, Default Mode Network, and sensorimotor integration. Comprehension deficits result from focal lesions and left hemisphere stroke, with connectivity training showing potential. General cognition studies emphasize the role of working memory, connectivity patterns predicting ischemic attacks, and cognitive impairment post-subtentorial strokes. Praxis studies highlight the importance of spared left hemisphere regions, interhemispheric connectivity, and cognitive mechanisms in complex figure copying tasks. The intricate relationship between complex cognitive abilities and brain networks is explored, revealing the impact of damage on verbal creativity, mental state judgments, affordance-based processing, and beta-band phase synchronization in memory retrieval. Strengths include a systematic search strategy and inclusion of original English studies. Limitations include the lack of statistical analysis due to heterogeneity and varying methodologies. The synthesis underscores the shift toward understanding brain function through network perspectives, combining neuroimaging with neuropsychological assessments. The integration of artificial intelligence offers promise in processing complex datasets. Future implications involve standardizing methodologies, interdisciplinary collaboration, and leveraging AI for personalized interventions, with broad applications in clinical, research, and policy domains.

In traumatic brain injury (TBI), mechanical forces trigger a series of detrimental processes in the affected brain, which eventually result in substantial neuronal death. TBI has thus become a leading cause of death and disability worldwide. Here we utilized organotypic hippocampal slice cultures from mice to simulate mild diffuse TBI, the most common type, in vitro. We specifically used this model to examine the potential of 17β-estradiol (E2), which is considered to be neuroprotective, to influence injury-induced events, such as astrocyte and microglia activation, and to reduce cell death, if applied acutely after TBI. We found that established consequences of mechanical brain injury are replicated in the model, as increased apoptosis was observed 8 h and PI-uptake was significantly enhanced 24 h after in vitro TBI in CA1 pyramidal layer. GFAP expression was not overall increased, but correlated with cell death, indicating a confined activation of astrocytes associated with cell injury. Similarly, no general increase of microglia was detected, but activated microglia was observed in the vicinity of dying cells. Notably, application of E2 (20 nM) increased GFAP expression after 48 h, but did not significantly reduce cell death at any of the studied time points. We conclude that the presented in vitro TBI model is generally suited to study processes triggered by diffuse mechanical forces acting on brain tissue. Our data further support a stimulating effect of E2 on GFAP expression in astrocytes, but they do not confirm a neuroprotective role of E2 in the early phase of TBI.

This work proposes using functional Near-Infrared Spectroscopy (fNIRS) as a non-invasive alternative to study the motor cortex's functional connectivity in Parkinson’s Disease (PD). The bilateral motor regions were covered with the fNIRS probe, and graph theoretical network analysis and network-based statistics were applied to investigate differences in network topology and specific sub-networks between groups. Small-world properties like clustering coefficient, characteristic path length, and small-world index were computed and compared between PD patients and controls across various sparsity thresholds. PD patients exhibited a lower clustering coefficient and small-world index than controls. Network-based statistics identified a disconnected, mostly bilateral subnetwork in the PD group comprising nine edges and ten nodes. Mean functional connectivity was positively correlated with both groups' clustering coefficient and small world index, albeit this correlation was greater in the control group. A strong coupling between these two properties suggests that greater functional connectivity within the subnetwork may cause a more effective functional motor network in controls. The results provide insights into alterations in functional connectivity and network organization in the motor cortex of individuals with PD, demonstrating the potential of fNIRS for studying the neural basis of symptoms in this disease.

In the realm of authentication, biometric verification has gained widespread adoption, especially within high-security user authentication systems. Although convenient, existing biometric systems are susceptible to a number of security vulnerabilities, including spoofing tools such as gummy fingers for fingerprint systems and voice coders for voice recognition systems. In this regard, brainwave-based authentication has emerged as a novel form of biometric scheme that has the potential to overcome the security limitations of existing systems while facilitating additional capabilities, such as continuous user authentication. In this study, we focus on a data-driven approach to Electroencephalography (EEG)-based authentication, guided by the power of machine learning algorithms. Our methodology addresses the fundamental challenge of distinguishing real users from intruders by training classification algorithms to the unique EEG signatures of every individual. The system is characterized by its convenience, ensuring real-time applicability without compromising its efficiency. By employing a commercially available single-channel EEG sensor and extracting a set of 8 power spectral features (delta [0–4 Hz], theta [4–8 Hz], low alpha [8–10 Hz], high alpha [10–12 Hz], low beta [12–20 Hz], high beta [20–30 Hz], low gamma [30–60 Hz], high gamma [60–100 Hz]), a commendable mean accuracy of 85.4% was achieved.

Traumatic brain injuries (TBIs) are characterized by widespread complications that exert a debilitating effect on the well-being of the affected individual. TBIs are associated with a multitude of psychiatric and medical comorbidities over the long term. Furthermore, no medications prevent secondary injuries associated with a primary insult. In this perspective article, we propose applying graph theory via the construction of disease comorbidity networks to identify high-risk patient groups, offer preventive care to affected populations, and reduce the disease burden. We describe the challenges associated with monitoring the development of comorbidities in TBI subjects and explain how disease comorbidity networks can reduce disease burden by preventing disease-related complications. We further discuss the various methods used to construct disease comorbidity networks and explain how features derived from a network can help identify subjects who might be at risk of developing post-traumatic comorbidities. Lastly, we address the potential challenges of using graph theory to successfully manage comorbidities following a TBI.