Brain Sciences最新文献

Background: Acute subdural hematoma (aSDH) is associated with a high risk of epilepsy, a complication linked to poor outcomes. Craniotomy is a known risk factor, with an epilepsy incidence of approximately 25%. This study evaluated radiomic features from preoperative CT scans to predict epilepsy risk in aSDH patients undergoing craniotomy.

Methods: A retrospective analysis of 178 adult aSDH patients treated between 2016 and 2022 identified 64 patients meeting inclusion criteria. Radiomic features (e.g., Feret diameter, elongation, flatness, surface area, and volume) from preoperative CT scans within 24 h of surgery were analyzed alongside clinical factors, including cardiac comorbidities, pupillary response, SOFA score, age, and anticoagulation status.

Results: Of the 64 patients, 18 (28%) developed generalized seizures. Univariate analysis showed significant associations with Feret diameter (p = 0.045), elongation (p = 0.005), cardiac comorbidities (p = 0.017), and SOFA score (p = 0.036). ROC analysis showed excellent discriminatory ability for elongation (AUC = 0.82). Multivariate analysis identified elongation as an independent predictor (p = 0.003); elongation ≥ 1.45 increased seizure risk 7.78-fold (OR = 7.778; 95% CI = 1.969-30.723).

Conclusions: Radiomic features, particularly elongation, may help predict epilepsy risk in aSDH patients undergoing craniotomy. Prospective validation is needed.

Background: Emerging evidence suggests that peripheral immunoinflammatory responses contribute to Alzheimer's disease (AD) pathogenesis, and endothelial cells (ECs) are involved in these responses. Nevertheless, the potential molecular mechanisms and signaling pathways by which ECs modulate peripheral immunoinflammatory responses and thus contribute to AD pathogenesis are not fully understood.

Methods: The single-cell RNA sequencing dataset GSE157827 was analyzed, and AD key genes were screened using LASSO regression and random forest algorithms. Functional enrichment analyses of these AD key genes were conducted using gene set enrichment analysis (GSEA) and gene set variation analysis. Immune cell infiltration analyses for AD key genes were performed using single-sample GSEA, and their correlations with immunoinflammatory factors were assessed using the TISIDB database. Peripheral blood RNA sequencing data from our cohort were utilized to validate the expression patterns of EC-related AD key genes in peripheral blood and to investigate their association with cognition.

Results: ECs are the most significant contributors to AD among all brain cell subpopulations. For the first time, the EC-related genes EIF1 and HSPA1B were identified as key genes associated with AD progression. These two EC-related key genes may participate in AD pathogenesis by modulating peripheral immunoinflammatory responses. The levels of EIF1 and HSPA1B were significantly altered in the peripheral blood during AD progression, and EIF1 levels correlated with cognitive functions in AD clinical continuum patients.

Conclusions: These findings underscore the critical roles of the EC-related genes EIF1 and HSPA1B in AD pathogenesis and their potential as biomarkers for this disease.

Background: Hypermobility Ehlers-Danlos syndrome (hEDS) is a clinical condition characterized by hypermobility and tissue fragility and is associated with chronic pain. The present study aimed to investigate the associations between affect regulation, pain perception, and psychophysical dimensions as well as alexithymic characteristics in the pathological range. Methods: Twenty-five hEDS patients completed a socio-anamnestic questionnaire as well as the Brief Pain Inventory (BPI), the 36-Item Short Form Survey (psychophysical health), the Difficulties in Emotion Regulation Scale (DERS), and the 20-item Toronto Alexithymia Scale (affect regulation). Results: Correlational analysis showed several negative significant associations between the SF-36, DERS, and TAS-20. The BPI showed few significant associations with both affect regulation measures. Moreover, a relationship between psychological dimensions and the time since diagnosis emerged. A total of 28% of participants reported TAS-20 scores in the clinical range and 36% reported scores in the borderline area. Discussion: Patients with hEDS seem to show high alexithymia levels; pain seems to interfere with the practical aspects of daily life and may reduce an individual's awareness of their emotional capabilities. The perception of heightened pain has a stronger impact on emotional resources when it interferes with affective life than when it interferes with practical life. Finally, delayed diagnoses of hEDS entail psychological consequences such as alexithymia. Conclusions: The present findings highlight the importance of promoting affect regulation capabilities through the implementation of psychological intervention programs for patients suffering from this medical condition.

The global rise in mental health-related disorders represents a significant health and wellbeing challenge, imposing a substantial social and economic burden on individuals, communities, and healthcare systems. According to the World Health Organization, one in four people globally will be affected by mental or neurological disorders at some point in their lives, highlighting a significant global health concern that warrants carefully considered and innovative responses. While mental health challenges arise from complex, multifaceted factors, emerging research indicates that the built environment-the architecture of our homes, workplaces, and public spaces-may exert a critical but underappreciated influence on mental health outcomes. This paper outlines a novel theoretical framework for how visual stressors in the built environment might trigger neurophysiological stress responses via the HPA and SAM axes, potentially contributing over time to allostatic load. In this paper, it is proposed that chronic physiological strain can alter neuroplastic processes and neurogenesis in key brain regions-such as the hippocampus, prefrontal cortex (PFC), anterior cingulate cortex (ACC), and amygdala-thereby affecting cognitive health, emotional regulation, and overall mental wellbeing. Drawing on the principle of neurosustainability, this paper suggests that long-term exposure to stress-inducing environments may create feedback loops, particularly involving the amygdala, that have downstream effects on other brain areas and may be linked to adverse mental health outcomes such as depression. By presenting this framework, this paper aims to inspire further inquiry and applied experimental research into the intersection of neurophysiology, mental health, and the built environment, with a particular emphasis on rigorous testing and validation of the proposed mechanisms, that may then be translated into practical architectural design strategies for supporting health and wellbeing. In doing so, it is hoped that this work may contribute to a more holistic approach to improving mental health that integrates the creation of nurturing, resilient spaces into the broader public health agenda.

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas.

Growth mindset, which asserts that intelligence and abilities can be cultivated through effort and learning, has garnered substantial attention in psychological and educational research. While the psychological and behavioral impacts of growth mindset are well-established, the underlying neural mechanisms remain relatively underexplored. Furthermore, there is a lack of comprehensive reviews synthesizing the neural evidence on growth mindset, hindering a fuller understanding of this concept. This scoping review aims to synthesize existing empirical studies on the neural mechanisms of growth mindset, focusing on research objectives, methods, and participant characteristics. A total of 15 studies were reviewed, revealing six primary research objectives: (1) neural mechanisms of error and feedback processing, (2) domain-specific mindsets, (3) neural changes resulting from mindset interventions, (4) mindsets and grit, (5) the neuroanatomy of mindsets, and (6) neural mechanisms of stereotype violation, with error and feedback processing being the most frequently investigated. Ten of the 15 studies employed EEG, while other techniques included structural MRI, task-based fMRI, and resting-state fMRI, with the majority of research focusing on adult populations. Although the existing literature offers valuable insights, further research is needed to explore additional aspects of mindsets, particularly in children, and to refine the methodologies used to investigate the neural mechanisms underlying growth mindset.

Background: The objective of this study was to investigate the prevalence of discharge against medical advice (DAMA) among schizophrenia patients in Germany and to identify factors associated with the risk of DAMA.

Methods: This multicenter cross-sectional study was based on data from the IQVIA hospital database, which contains records from 36 hospitals across Germany. This study included all hospitalizations for patients with a primary or secondary diagnosis of schizophrenia between January 2019 and December 2023. Multivariable logistic regression analyses adjusted for age, sex, primary or secondary schizophrenia diagnosis, as well as codiagnoses, were conducted to assess the associations between demographic and clinical variables and DAMA.

Results: A total of 7663 hospitalization cases (mean age: 49.5 years, 40.6% female) were included in the study. The DAMA rate was 31.1% in patients with schizophrenia as the primary diagnoses and 6.0% in patients with schizophrenia as a secondary diagnosis. Younger age (i.e., adjusted odds ratio (aOR): 7.44; 95% CI: 4.35-12.73 in the age group 18-30; aOR: 6.63; 95% CI: 3.89-11.29 in the age group 31-40; aOR: 5.59; 95% CI: 3.28-9.54 in the age group 41-50), schizophrenia as the primary diagnosis (aOR: 3.61; 95% CI: 3.05-4.26), alcohol-related disorders (aOR: 1.68; 95% CI: 1.38-2.04), and cannabis-related disorders (aOR: 1.43; 95% CI: 1.18-1.72) were significantly associated with an increased risk of DAMA.

Conclusions: This study highlights the high prevalence of DAMA among hospitalized schizophrenia patients and identifies the important factors (i.e., younger age, alcohol-related disorders, and cannabis-related disorders) associated with DAMA risk. Additional studies are recommended for further exploration into the reasons for DAMA.

Current standard of care treatment for patients with spine tumors includes multidisciplinary approaches, including the following: (1) surgical tumor debulking, epidural spinal cord decompression, and spine stabilization techniques; (2) systemic chemo/targeted therapies; (3) radiation therapy; and (4) surveillance imaging for local disease control and recurrence. Titanium pedicle screw and rod fixation have become commonplace in the spine surgeon's armamentarium for the stabilization of the spine following tumor resection and separation surgery. However, the high degree of imaging artifacts seen with titanium implants on postoperative CT and MRI scans can significantly hinder the accurate delineation of vertebral anatomy and adjacent neurovascular structures to allow for the safe and effective planning of downstream radiation therapies and detection of disease recurrence. Carbon fiber-reinforced polyetheretherketone (CFR-PEEK) spine implants have emerged as a promising alternative to titanium due to the lack of artifact signals on CT and MRI, allowing for more accurate and safe postoperative radiation planning. In this article, we review the tenants of the surgical and radiation management of spine tumors and discuss the safety, efficacy, and current limitations of CFR-PEEK spine implants in the multidisciplinary management of spine oncology patients.

In the original publication [...].

Statistical learning (SL), the ability to extract patterns from the environment, has been assumed to play a central role in whole cognition, particularly in language acquisition. Evidence has been gathered, however, from behavioral experiments relying on simplified artificial languages, raising doubts on the generalizability of these results to natural contexts. Here, we tested if SL is affected by the composition of the speech streams by expositing participants to auditory streams containing either four nonsense words presenting a transitional probability (TP) of 1 (unmixed high-TP condition), four nonsense words presenting TPs of 0.33 (unmixed low-TP condition) or two nonsense words presenting a TP of 1, and two of a TP of 0.33 (mixed condition); first under incidental (implicit), and, subsequently, under intentional (explicit) conditions to further ascertain how prior knowledge modulates the results. Electrophysiological and behavioral data were collected from the familiarization and test phases of each of the SL tasks. Behavior results revealed reliable signs of SL for all the streams, even though differences across stream conditions failed to reach significance. The neural results revealed, however, facilitative processing of the mixed over the unmixed low-TP and the unmixed high-TP conditions in the N400 and P200 components, suggesting that moderate levels of entropy boost SL.