Frontiers in Neurology最新文献

Insomnia features include persistent difficulties in sleep initiation and sleep maintenance, leading to significant daytime dysfunction and diminished quality of life. While acupuncture is increasingly utilized in the management of insomnia, its specific efficacy in alleviating daytime dysfunction remains inadequately substantiated. This systematic review aims to address this evidence gap. A total of 5,037 articles from 6 electronic databases were searched and screened. Data analysis was performed using Review Manager 5.4 software and Stata 13. The Cochrane tool for assessing the risk of bias in randomized trials (RoB2) and GRADE were used to evaluate the quality of the RCTs and the evidence. Eighteen randomized controlled trials (RCTs) encompassing a total of 1,767 patients were analyzed. Data of the Montreal Cognitive Assessment (MoCA), Auditory Verbal Memory Test (AVMT), and Fatigue Scale-14 (FS-14) scores were extracted and aggregated, along with the scores of the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI). Moderate-quality evidence showed that acupuncture therapies could not only significantly reduce insomnia severity but also enhance memory ability and relieve cognitive impairment. Low-quality evidence presented that acupuncture could improve sleep quality and reduce fatigue in patients with chronic primary insomnia. Although heterogeneity was observed across several synthesized outcomes, the results were robust.

Systematic review registration: The research protocol was registered in PROSPERO (ID: CRD42023442722).

Background: The non-motor symptoms of Parkinson's patients seriously affect their quality of life. This meta-analysis intended to systematically examine the beneficial effect of bright light therapy (BLT) on non-motor symptoms in individuals with Parkinson's disease (PD).

Methods: A systematic search was conducted in PubMed, Cochrane Library, Web of Science, Embase, Ovid Medline, China National Knowledge Infrastructure (CNKI), WanFang VIP and CBM to comprehensively collect RCTS and NRCTS relevant to BLT for Parkinson's disease. Data extraction and literature screening were carried out separately by two researchers, and used the RoB2 tool and ROBINS-I tool to evaluate the risk of bias for the two types of studies, respectively. Software such as RevMan5.4 and Stata 18.0 were used to analyze the data.

Results: Compared with dim light, BLT has a considerable benefit in improving the nighttime sleep of people with Parkinson's disease. However, its therapeutic effects on depression, anxiety, exhaustion, cognition, sleep quality and quality of life are not superior to those of dim light. Moreover, the distance of the light source will alter the therapeutic effect of BLT on nighttime sleep and daytime sleepiness.

Results: Compared with dim light, BLT has a considerable benefit in improving the nighttime sleep of people with PD. However, its therapeutic effects on depression, anxiety, fatigue, cognition, sleep quality and quality of life are not superior to those of dim light. Moreover, the distance of the light source will alter the therapeutic effect of BLT on nighttime sleep and daytime sleepiness.

Conclusion: BLT can be used to treat the non-motor symptoms of PD and may become a practical option for home self-management. However, the ideal therapy parameters (such as intensity, distance, cycle, etc.) still need further research.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251047102, identifier (CRD420251047102).

Background: Early and accurate identification of mild cognitive impairment (MCI) is crucial for timely intervention and preventing further cognitive decline. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, portable tool for clinical screening, but its diagnostic accuracy is often constrained by single-paradigm approaches and small sample sizes. To address this limitation, this study aimed to develop and validate an efficient early MCI screening model by integrating large-sample fNIRS data from resting-state and 1-back task paradigms using ensemble machine learning, thereby enhancing the accuracy and reliability of early MCI diagnosis.

Methods: A total of 462 right-handed participants (185 MCI patients and 277 healthy controls, aged 58 -87 years) were included in the final analysis after screening, with MCI diagnosis jointly determined by two experienced neurologists based on Petersen's criteria. fNIRS signals were collected during resting-state and 1-back task sessions; after preprocessing in MATLAB, features were extracted from oxygenated hemoglobin (HbO) signals of both paradigms.

Results: Feature selection was performed via a gradient boosting classifier based on feature importance scores, resulting in 108 selected features. Five classifiers were trained and evaluated using 10-fold cross-validation. The integrated dataset combining resting-state and 1-back task features outperformed the single-paradigm datasets: the Neural Network model on this integrated dataset achieved an accuracy of 86.49%, sensitivity of 94.74%, specificity of 77.78%, and Area Under the Curve (AUC) of 93.49%. In contrast, the Nearest Neighbor model on the resting-state dataset and the Decision Tree model on the 1-back task dataset yielded accuracies of 70.27% and 75.68%, respectively. Group classification using MoCA scores achieved an accuracy of 86.55%, which was comparable to single-paradigm machine learning models but inferior to the integrated model.

Discussion: This study demonstrates the value of a large-sample, data-driven approach and multi-paradigm feature integration in fNIRS-based MCI screening, providing an efficient diagnostic model for clinical application.

Clinical trial registration: https://www.chictr.org.cn/showprojEN.html?proj=192047.

Introduction: Parkinson's disease (PD) is a progressive neurological disorder whose early symptoms often remain undetected, making timely diagnosis challenging. Machine learning offers strong algorithms to detect subtle speech-based biomarkers that are impossible to detect by standard methods.

Methods: In this article, we proposed an Enhanced Ensemble Feature Selection Algorithm (EEFSA) which combines filter, wrapper, and embedded approaches to extract the best informative features, eliminate redundancy, and improve classification performance. The proposed work has tested on two benchmark audio based datasets, such as Dataset-1 (46 features, 80 samples), where EEFSA reduced the features to 20 features, and Dataset-2 (754 features, 252 samples), where EEFSA reduced the features to 40 features. Nine machine learning classifiers were tried out and the best of them were combined into a stacking ensemble with logistic regression as the meta-classifier.

Results: Experiments show that EEFSA-driven dimensionality reduction not only enhanced accuracy of classification but also reduced training time considerably and minimized over fitting. The Stacking Ensemble Classifier Model (SECM) deployed on the basis of the proposed method achieved accuracy of 86.67% and 89.95% on Dataset-1 and Dataset-2, respectively, and outperformed individual classifiers in all experiments.

Conclusion: Overall, this work provides EEFSA-driven stacking as a new and efficient method of feature selection and ensemble learning combination for Parkinson's disease classification. The proposed EEFSA-SECM framework achieves effective classification accuracy, competitive training/testing times, and improved AUC scores on two benchmark datasets, establishing it as an effective and efficient approach for Parkinson's disease diagnosis.

Background: Migraine predominantly affects women with prolonged and more severe episodes that severely compromise their quality of life. Despite its burden, diagnostic-therapeutic pathways specifically designed for women are currently lacking, and care for women with migraine remains fragmented. By expanding the results obtained from a recent Delphi consensus study, we aim to identify key unmet needs and propose expert-driven recommendations to address female-specific migraine care in Italy.

Methods: We conducted a national survey in which the items used in the previous Delphi study were administered to a broader cohort of Italian healthcare professionals (HCPs). The survey was administered to 125 HCPs; of those, 92% were neurologists.

Results: Diagnostic delays, insufficient multidisciplinary collaboration, and heterogeneous care emerged for women with migraine across Italy. Respondents endorsed parental awareness and socio-behavioral approaches for pediatric patients, careful evaluation of thrombotic risk for contraceptive choice, systematic assessment of migraine history and new-onset migraine during pregnancy and breastfeeding, and appropriate selection of hormone replacement therapy during menopause. Close monitoring of migraine symptoms is encouraged in oncological patients and women undergoing assisted reproduction, although evidence and guidelines for these patient subsets are currently lacking. Across all settings, the neurologist emerged as the central medical reference figure from adolescence onward, working in close collaboration with gynecologists, pediatricians, and oncologists as appropriate. Strengthening continuity of care, enhancing multidisciplinary collaboration, expanding professional training, and promoting awareness initiatives are considered the key strategies to optimize migraine care in women.

Conclusions: Based on the respondents' answers, we propose practical frameworks that outline migraine care pathways tailored to specific female life stages and health conditions. Efforts should prioritize the design of targeted studies to overcome the identified evidence gaps and pave the way for more structured models.

Background: Hepatic encephalopathy (HE) involves complex neurobiological changes that are often difficult to quantify using conventional MRI. This study aims to utilize Neurite Orientation Dispersion and Density Imaging (NODDI) combined with Gray-matter Based Spatial Statistics (GBSS) to characterize microstructural alterations in patients with HE and explore their relationship with clinical biochemical markers, specifically within the globus pallidus (GP).

Methods: Thirty-three patients with HE and 31 healthy controls underwent 3 T MRI including a multi-shell diffusion protocol for NODDI. GBSS was performed to assess differences in the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI). Pearson correlation analyzed relationships between GP NODDI parameters and blood biochemical indices.

Results: HE patients exhibited significantly decreased NDI across widespread cortical and sub-cortical regions (frontal, parietal, temporal, cingulate, insula, thalamus) and increased ODI in the posterior cerebellum/vermis. Exploratory ROI analysis of the globus pallidus (GP)-a region known for manganese deposition but showing no significant group-level differences in this study- revealed that, the NDI of the right GP showed positive correlations with indirect bilirubin and prothrombin international normalized ratio (all uncorrected p < 0.05), while the ODI of the left GP positively correlated with hemoglobin concentration (uncorrected p = 0.046).

Conclusion: NODDI reveals extensive microstructural alterations consistent with reduced neurite density index and cerebellar disorganization in HE. The dissociated correlation patterns of GP NDI and ODI with distinct blood markers may be compatible with a hypothetical "double-hit" pathophysiological model: toxic metabolite accumulation may drive cellular swelling (increased NDI), while systemic factors like anemia may reduce structural complexity (decreased ODI). However, these exploratory associations do not allow causal inference. These findings highlight NODDI could be a useful tool for monitoring the progression and metabolic impact of HE.

Objective: Given the limitations of current treatments for Alzheimer's disease (AD), this study aims to comprehensively evaluate the therapeutic efficacy of human umbilical cord mesenchymal stem cells (hUCMSCs) in AD mouse models through a systematic review and meta-analysis. Additionally, we explore the impact of transplantation dose and route on treatment outcomes to identify the optimal window for clinical application.

Methods: In accordance with the PRISMA guidelines, we systematically searched four major databases to identify randomized controlled trials involving hUCMSCs in AD mouse models. We used the standardized mean difference (SMD) to synthesize effect sizes and performed subgroup analyses based on pre-defined transplantation routes and doses.

Results: A total of 13 studies were included in the analysis. The meta-analysis revealed that hUCMSCs transplantation significantly improved spatial learning and memory in AD model mice, with a marked reduction in escape latency (SMD = -2.55; 95% CI: -3.34 to -1.75; I ² = 77.9%, random-effects model). Additionally, it significantly lowered brain β-amyloid levels (SMD = -5.34; 95% CI: -7.21 to -3.47; I ² = 80.3%), increased brain-derived neurotrophic factor (SMD = 4.25; 95% CI: 3.18 to 5.31), and reduced neuronal apoptosis (SMD = -4.96; 95% CI: -6.52 to -3.41). Subgroup analyses further revealed that efficacy was significantly dose- and route-dependent. For cognitive improvement, intravenous injection of medium to high doses (≥1 × 10⁶ cells) was most effective and robust. For amyloid-β clearance, low-dose administration via intravenous, lateral ventricle, and cortical routes showed significant efficacy, whereas bilateral hippocampal injection did not yield significant benefits.

Conclusion: Human umbilical cord mesenchymal stem cells can improve behavioral and pathological outcomes in AD mouse models via multiple mechanisms of action. The intravenous route using medium to high doses emerges as a critical factor for achieving optimal effects, providing important evidence and informing future experimental design and clinical translational research.

Objective: To systematically review the outcome constructs, modeling characteristics, and methodological quality of existing sepsis-associated brain dysfunction (SABD) risk prediction models, with the aim of explaining why current models are difficult to reproduce or translate into practice, and of proposing standardized directions for future research.

Methods: A systematic review was conducted by searching CNKI, Wanfang, VIP, SinoMed, PubMed, CINAHL, Cochrane Library, Embase, and Web of Science from database inception to April 2025. Studies developing or validating SABD risk prediction models were included, with outcomes defined as sepsis-associated encephalopathy (SAE) or sepsis-associated delirium (SAD). Model characteristics were extracted according to the CHARMS checklist, and methodological quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST).

Results: Twelve studies involving 24 risk prediction models were included, of which four studies evaluated SAD as the outcome and eight evaluated SAE. Substantial heterogeneity was observed in outcome definitions, modeling strategies, and variable selection approaches. Calibration was reported in 10 studies, internal validation in nine studies, and both internal and external validation in one study. According to PROBAST, three studies had high applicability concerns and nine had low applicability concerns. All included studies were assessed as having a high risk of bias, predominantly in the analysis domain.

Conclusion: Current risk prediction modeling studies for SAD and SAE remain exploratory, and high risk of bias together with insufficient validation limits their reliable clinical translation. Future research should adhere to the PROBAST and TRIPOD guidelines, conduct multicenter prospective studies, and standardize modeling and validation procedures.

Systematic review registration: https://www.crd.york.ac.uk/, identifier CRD420251014680.