BackgroundChronic fatigue syndrome (CFS) is a complex disorder characterized by persistent fatigue, sleep disturbance, and cognitive impairment. It substantially impairs both physical and mental health. Tai Chi, a traditional Chinese mind–body exercise, may improve symptoms in patients with CFS. However, its neural mechanisms remain unclear. This study investigated the effects of Tai Chi on spontaneous brain activity in patients with CFS using resting-state functional magnetic resonance imaging (rs-fMRI) and amplitude of low-frequency fluctuations (ALFF).MethodsSixty patients with CFS were randomly assigned in a 1:1 ratio to either a Tai Chi group or a health education group; 57 participants completed both fMRI scans and fatigue assessments and were included in the complete-case analysis. Both groups underwent a 12-week intervention consisting of three 60-min sessions per week. Adherence was monitored using weekly logs. Fatigue severity was assessed before and after the intervention using the Multidimensional Fatigue Inventory (MFI-20). Resting-state fMRI data were acquired at both time points. Preprocessing included motion correction, normalization to Montreal Neurological Institute space, and spatial smoothing. ALFF was calculated to quantify regional spontaneous neural activity. Correlation analyses were performed to assess associations between changes in ALFF and MFI-20 scores. Harms were monitored throughout the intervention period.ResultsFifty-seven participants were included in the complete-case analysis (Tai Chi, n = 29; health education, n = 28). Compared with health education, Tai Chi produced greater reductions in MFI-20 total score from baseline to week 12 (between-group change difference, −9.50 points; 95% CI, −12.31 to −6.70; Cohen's d = −1.83; P < 0.001). Greater improvements were also observed in overall fatigue, physical fatigue, mental fatigue, activity reduction, and interest reduction, with between-group differences ranging from −1.26 to −2.44 points. In imaging analyses, Tai Chi was associated with increased ALFF in prefrontal, hippocampal/striatal, and supplementary motor regions and decreased ALFF in occipital and temporal regions, using voxel-level P < 0.001 and cluster-level family-wise error correction. Exploratory brain–behavior analyses showed that increases in ALFF in the left medial superior frontal gyrus and left dorsolateral superior frontal gyrus were associated with greater reductions in MFI-20 scores, whereas decreases in ALFF in the left inferior occipital gyrus were associated with fatigue improvement. No falls, injuries, severe post-exertional malaise, or intervention-related medical events were reported through weekly participant logs or supervised-session monitoring.ConclusionA 12-week Tai Chi intervention was associated with greater fatigue improvement than health education and with region-specific changes in resting-state spontaneous brain activity in patients with CFS. Tai Chi was associated with reduced fatigue and altered spontaneous brain activity in prefrontal, hippocampal, motor, and sensory-related regions in patients with CFS. These findings suggest that Tai Chi may modulate fatigue-related brain function, which may contribute to improvements in fatigue severity. Future studies should replicate and extend existing network-level findings using prespecified, adequately powered connectivity and mediation analyses with active exercise comparators and longer-term follow-up.Chinese clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=223860, ChiCTR2400082268; March 26, 2024.
Coronary heart disease (CHD) is commonly conceptualized as a disorder of atherosclerotic plaque burden, myocardial oxygen supply-demand mismatch and thrombosis. This framework remains indispensable, yet it does not fully explain stress-induced ischemia, persistent symptoms in the absence of obstructive lesions, or neuropsychiatric and cognitive sequelae after acute coronary events. Evidence increasingly indicates that CHD is embedded in a bidirectional brain-heart axis in which central autonomic, neuroendocrine, immune, vascular, metabolic, sleep-circadian and extracellular vesicle-mediated signals interact over time. In the brain-to-heart direction, psychological stress and altered activity within central autonomic networks may promote sympathetic activation, vagal withdrawal, hypothalamic-pituitary-adrenal signaling, endothelial dysfunction, platelet activation, coronary microvascular dysregulation and inflammatory acceleration of atherosclerosis. Mental stress-induced myocardial ischemia is a clinically relevant example of this top-down pathway and has been associated with adverse outcomes in patients with established CHD. In the heart-to-brain direction, coronary ischemia and myocardial infarction may affect brain function through visceral afferent signaling, systemic and neuroinflammation, cerebral hypoperfusion, blood-brain barrier disruption and cardiac extracellular vesicles. These processes may contribute to depression, anxiety, cognitive impairment and altered interoceptive control after acute coronary events, thereby creating feedback loops that can worsen cardiovascular risk. This review critically synthesizes clinical, observational, neuroimaging, and experimental evidence concerning CHD as a bidirectional brain-heart axis disorder. We distinguish established clinical associations from emerging mechanistic pathways, highlight major methodological limitations and unresolved controversies, and discuss the current level of evidence supporting candidate biomarkers and therapeutic strategies. We argue that future CHD management should complement plaque- and ischemia-oriented strategies with assessment of autonomic function, mental health, inflammatory tone, sleep-circadian biology, cerebrovascular health and neuroimmune signaling. Such integration may help identify patients whose risk is driven not only by coronary anatomy, but also by maladaptive brain-heart communication.
High-performance computing (HPC) is critical for simulating large-scale neural networks with detailed biophysics, yet deploying these simulations across heterogeneous computing infrastructures remains a significant challenge due to software dependencies and hardware variability. This study introduces a framework utilizing containerization technology to ensure scalable and reproducible simulations by encapsulating the entire software stack, including compilers, MPI libraries, and GPU toolchains, within a portable image. We created a spiking network model with millions of neurons and synapses by replicating the cerebellar neural circuit module and performed benchmarking with it. Our results demonstrate that containerized simulations introduce little performance penalty compared to native installations while delivering identical results across multiple systems with minimal effort, providing a robust solution for portability. This framework can accelerate the scalable development of spiking network models, facilitate the precise reproduction of complex workflows and simplify collaboration, addressing major barriers in modern computational neuroscience research.
BackgroundPost-traumatic headache (PTH) is one of the most common and disabling sequelae of traumatic brain injury (TBI). Neuroimaging has been increasingly used to evaluate acute injury, identify structural and functional abnormalities, and explore the mechanisms underlying persistent headache after TBI. However, the global development and research trends in this field remain unclear. This study aimed to map the knowledge structure and emerging trends of neuroimaging research related to PTH after TBI.MethodsA comprehensive literature search was conducted in the Web of Science Core Collection (WoSCC), Scopus, and PubMed from database inception to December 31, 2025. WoSCC and Scopus were used as the principal bibliometric dataset, while PubMed was used for supplementary validation. Bibliometrix, VOSviewer, and CiteSpace were used to analyze publication trends, countries, institutions, journals, authors, cited references, keyword co-occurrence, thematic evolution, and trend topics.ResultsAfter merging WoSCC and Scopus records, 2,519 documents from 938 sources were included in the main analysis, and 539 PubMed records were used for supplementary validation. Annual publication output showed an overall upward trend over the study period. The United States and Harvard University were the most productive country and institution, respectively, whereas the Journal of Neurotrauma and Schwedt TJ showed the highest source and author influence. Within the broader literature encompassed by this search, highly cited publications included studies on CT-based acute brain injury assessment, pediatric risk stratification, PTH diagnostic classification, and post-concussion symptoms. Keyword and thematic analyses revealed a gradual transition from acute imaging evaluation toward advanced neuroimaging, outcome assessment, prognosis, and rehabilitation.ConclusionWithin this broad interdisciplinary multi-database bibliometric dataset linking TBI, PTH and neuroimaging, we identified a long-standing foundation in acute TBI imaging and related symptom assessment, alongside an increasingly visible research trajectory focused on advanced neuroimaging of persistent symptoms, mechanisms, prognosis, and rehabilitation-oriented management.
IntroductionPost-stroke cognitive impairment (PSCI) affects 30%–70% of stroke survivors and represents a significant barrier to functional recovery and quality of life. This systematic review synthesizes current evidence on multimodal interventions targeting neuroplastic mechanisms to ameliorate cognitive dysfunction following stroke.MethodsFollowing PRISMA 2020 guidelines, we systematically searched PubMed, Web of Science, Cochrane Library, and Embase for randomized controlled trials published between January 2020 and October 2025. Forty-seven studies met inclusion criteria, encompassing 3,842 participants across diverse intervention modalities including non-invasive brain stimulation (transcranial direct current stimulation, repetitive transcranial magnetic stimulation), cognitive rehabilitation, virtual reality training, computer-assisted cognitive training, and combined multimodal approaches.ResultsNarrative synthesis revealed that transcranial direct current stimulation combined with cognitive training consistently yielded the largest improvements in global cognitive function across included trials, followed by repetitive transcranial magnetic stimulation protocols. Several studies also explored pharmacological agents (e.g., cholinesterase inhibitors) as adjunctive components within multimodal protocols.DiscussionNeuroplasticity mechanisms underlying these improvements include enhanced synaptic plasticity, modulation of long-term potentiation, neurogenesis in perilesional regions, functional reorganization of cortical networks, and restoration of interhemispheric balance. Early intervention initiation (within 3 months post-stroke) was associated with enhanced outcomes across modalities. Virtual reality and computer-assisted training demonstrated moderate efficacy with superior patient engagement and accessibility. Evidence supports multimodal, personalized rehabilitation protocols integrating brain stimulation with behavioral interventions to optimize neuroplastic potential. Future research should evaluate combined rehabilitation approaches at the neural level, assess pharmacological treatment effects on neural plasticity, and investigate long-term maintenance of cognitive gains. This review provides evidence-based guidance for clinicians implementing neuroplasticity-informed rehabilitation strategies for post-stroke cognitive recovery.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier [CRD420261382284].
IntroductionParkinson’s disease (PD) main pathological feature involves degeneration of dopamine (DA) neurons in the brain substantia nigra (SN). Female gonadal steroids are shown to be neuroprotective in animal models of PD and tested mainly in male and less in female animals. Since most women with PD are in menopause the present study tested ovariectomized (OVX) female mice as a model of the hormonal condition of menopause. This study sought the neuroprotective effect of the selective estrogen receptor modulator (SERM) raloxifene and progesterone alone and in combination to protect DA neurons in 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP)-lesioned mice as a model of PD.MethodsMice were treated with vehicle, raloxifene (2.5 mg/kg, b.i.d., subcutaneous), progesterone (1 μg, b.i.d., subcutaneous) and their combination for 10 days and administered MPTP (5.5 mg/kg, intraperitoneal) or saline on the 5th day; brains and uteri were collected thereafter.ResultsRaloxifene treatment led to only small increases in uterine weights less than an average of intact mice uterine weights and no effect of progesterone. OVX MPTP-lesioned female mice showed a loss of striatal DA and metabolites content similarly prevented by treatments with raloxifene, progesterone and their combination. By contrast, striatal serotonin and metabolite remained unchanged. Striatal glial fibrillary acidic protein (GFAP), an astrogliosis marker, levels were elevated in MPTP-lesioned mice and this was similarly prevented with the hormonal treatment alone or in combination.ConclusionRaloxifene and progesterone treatment was neuroprotective without excessive uterine stimulation in OVX mice supporting repurposing of these drugs for PD and their high translational value.
Peripheral nerve recordings are a powerful research technique commonly performed using commercially available elastomeric cuff electrodes. However, emerging conformable thin-film technologies which offer advantages in device flexibility, feature miniaturisation, and electrode layout may represent an important alternative tool. In this study, we compare these two device classes as acute recording tools by implanting them side-by-side on rat sciatic nerves under anaesthesia and recording activity evoked by nociceptive muscle stimulation or proprioceptive ankle extension. We find that conventional elastomeric cuffs and conformable thin-film cuffs exhibit no statistically significant signal-to-noise ratio differences under this experimental regime, either under monopolar or referenced recording paradigms. When performing signal velocity analysis, conformable thin-film cuffs show a tendency to toward more frequently classifying recorded sensory activity as afferent, compared to conventional cuffs. Our findings support that conformable thin-film cuffs are competitive research tools for acute peripheral nerve recording.
IntroductionMesial temporal lobe epilepsy (MTLE) is a common focal epilepsy syndrome that may require surgery when seizures persist despite appropriate antiseizure medications. Although widespread gray matter (GM) abnormalities have been reported in MTLE, quantitative neuroimaging has not yet been implemented in the newly established epilepsy surgery programme in Fez, Morocco. This prospective observational exploratory pilot study evaluated the feasibility of implementing a whole-brain voxel-based morphometry (VBM) workflow using the Computational Anatomy Toolbox (CAT12), Statistical Parametric Mapping (SPM12), and ResectVol, and descriptively characterized GM differences before and after unilateral temporal lobectomy.MethodsFour patients with unilateral drug-resistant MTLE and eight age- and sex-matched healthy controls were prospectively recruited. Patients underwent preoperative and postoperative magnetic resonance imaging (MRI), whereas controls underwent one research MRI examination. Right-sided patient images were left–right flipped before tissue segmentation so that the epileptogenic and operated hemisphere corresponded to the left side in all patients. Exploratory whole-brain VBM maps were generated using general linear models for the contrasts controls > presurgical patients and presurgical > postsurgical patients; age, sex, and total intracranial volume (TIV) were entered as covariates in each model (voxel-level p < 0.001, uncorrected; cluster extent k > 50 voxels). These maps were used to identify anatomical regions for post hoc extraction of mean GM values. Regional values were compared using the Mann–Whitney-U-test and Wilcoxon signed-rank test.ResultsThe exploratory controls > presurgical contrast showed clusters in bilateral temporal, occipital, parietal, frontal, and sensorimotor regions. The exploratory presurgical > postsurgical contrast showed clusters predominantly in the ipsilateral temporal region. Post hoc paired comparisons showed lower postoperative mean GM values in the four studied regions. No postoperative GM increases were observed at the exploratory mapping threshold.ConclusionImplementing this VBM workflow was feasible within a newly established Moroccan epilepsy surgery programme. The findings are preliminary, descriptive, and hypothesis-generating; they do not establish disease mechanisms or postoperative reorganization. Larger, adequately powered longitudinal studies using dedicated repeated-measures models and multimodal quantitative MRI are required.
ObjectiveTo study the improvement of subjective visual quality in patients with a small amount of residual refraction and poor visual quality after small incision ienticule extraction (SMILE) surgery through visual cortex training.MethodsEleven subjects (22 eyes) aged 18–40 years who had undergone SMILE >6 months previously were prospectively enrolled. Subjects were trained 30 times, with the pre-training period as the baseline. Untrained patients from the same period were enrolled as controls.ResultsThe contrast sensitivity, best corrected visual acuity (BCVA), glare symptoms, and stereoscopic vision of the subjects was improved after training compared with before training. Compared with the eye with poor vision before training, the contrast sensitivity of the eye with good vision before training improved after training. There was no significant change in the no corrected visual acuity (NCVA) and refraction before and after training. After 12 months, contrast sensitivity was better in the visual cortex training group.ConclusionFor patients with a small amount of residual refractive error and reduced subjective visual quality after SMILE, RevitalVision training improved subjective visual function, particularly contrast sensitivity and related visual performance, without measurable changes in objective optical quality parameters.Clinical trial registrationIdentifier [ChiCTR1800014796].
ObjectivesTo identify the latent profile classification of cognitive function in elderly patients with chronic pain after stroke and explore the associated factors for patients in different categories.MethodsElderly patients with chronic pain after stroke hospitalized in the Department of Neurology of a hospital in Nanjing from August 2025 to January 2026 were selected as research subjects. Data were collected using a General Information Questionnaire, the Montreal Cognitive Assessment (MoCA), Hamilton Anxiety Scale (HAMA), Center for Epidemiological Studies Depression Scale (CES-D), Pain Catastrophizing Scale (PCS), and Elderly Social Participation Scale. Latent Profile Analysis (LPA) was used for patient classification, and multivariate logistic regression analysis was performed to identify predictive factors for different groups (P < 0.05).ResultsPatients’ cognitive function was classified into three latent profiles: High Cognitive Function-Low Abstraction Group, Moderate Cognitive Function-Low Orientation Group, and Low Cognitive Function Group. Logistic regression analysis showed that gender, pain type, pain intensity, anxiety, and depression were significant influencing factors of cognitive function across different categories (P < 0.05).ConclusionThere is significant population heterogeneity in the cognitive function of elderly patients with chronic pain after stroke. Medical staff should implement precise interventions for patients in different categories to delay cognitive decline and improve their quality of life.
IntroductionWhether rain, snow, and fog elicit qualitatively different driver-state dynamics remains unclear.MethodsThirty licensed drivers completed a simulator experiment under clear, rain, snow, and fog conditions, during which subjective scales, electrocardiograms, electroencephalograms, and vehicle data were acquired simultaneously. Six response pathways were constructed to characterize subjective load, operational fluctuation, conservative control, physiological arousal, electroencephalographic response, and system uncertainty. C4 spectral entropy gauge neural complexity, class-wise optimized reliability fusion prediction entropy measured system uncertainty in multimodal sensor responses, and within-subject centering, together with distance correlation, revealed how information is structured across modalities.ResultsAmong out-of-fold predictions over 6,456 windows, the class-wise optimized reliability fusion model achieved 91.9% accuracy and Macro-F1 of 0.919. All three adverse weather types significantly increased the subjective load, yet the dominant pathways differed. Rainfall increased cognitive load, snow led to synchronized increases in neural complexity and system uncertainty, and fog induced conservative compensation under visual restriction. Snow produced the strongest system-level response [class-wise optimized reliability fusion prediction entropy: r = 0.79, 95% confidence interval (CI): (0.62, 0.87); C4 spectral entropy: r = 0.53, 95% CI (0.21, 0.79)]. The four modalities formed an information-complementary structure.DiscussionThis study elucidates the information dynamics of driving states under adverse weather conditions and demonstrates that rain, snow, and fog occupy distinct regions within the information space, thereby providing a sensor-informed foundation for driver-state monitoring and graded warning systems in intelligent vehicles.
BackgroundCognitive impairment affecting memory and learning processes is a common non-motor feature of Parkinson’s disease (PD) and contributes substantially to functional decline and a reduced quality of life. Transcranial direct current stimulation (tDCS) has emerged as a potentially beneficial non-invasive neuromodulation technique capable of modulating cortical plasticity and enhancing cognitive and motor performance. However, its specific effects on memory- and learning-related outcomes in PD remain unclear.ObjectiveTo systematically review and synthesize the available evidence regarding the effects of tDCS on memory and learning processes in individuals with Parkinson’s disease.MethodsA systematic review was conducted following PRISMA guidelines, with the protocol registered in PROSPERO (CRD42024559548). PubMed, Scopus, and Web of Science were searched between September and December 2025. Randomized controlled trials in individuals with PD evaluating the effects of tDCS on memory-, learning-, or cognition-related outcomes were included. Study selection, data extraction, and risk-of-bias assessment (ROB-2) were performed systematically.ResultsNineteen studies (2006–2024) with sample sizes of 9–42 participants were included. Most protocols used 1–2 mA stimulation targeting the dorsolateral prefrontal cortex (DLPFC) and primary motor cortex (M1). Improvements were observed in working memory, verbal fluency, executive function, and processing speed, particularly in multi-session protocols combined with cognitive or motor training. M1 stimulation was associated with enhanced motor learning consolidation, whereas single-session interventions showed limited effects. Limitations include small samples, methodological heterogeneity, and limited follow-up.ConclusionCurrent evidence suggests potential effects of tDCS in memory and learning processes in PD, although evidence remains limited and heterogeneous, especially when delivered in repeated sessions and combined with training targeting frontostriatal dysfunction. However, variability in protocols and study quality limits generalizability. Larger, well-controlled trials with standardized designs are needed to establish robust clinical recommendations.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024559548, identifier CRD42024559548.
Motor Imagery-based (MI) Electroencephalography (EEG) has emerged as a leading solution in non-invasive Brain-Computer Interface (BCI) systems, leveraging its strong motor intention correlation to enable reliable neural decoding. However, practical implementation of MI confronts three persistent challenges: low signal-to-noise ratio, substantial variability across subjects or over time, and inherent signal nonstationarity. These fundamental limitations continue to hinder the widespread adoption and operational reliability of MI BCI systems. Despite advances in cross-variability decoding methods, there is a lack of systematic syntheses to guide technological evolution in MI BCI. To address these challenges, this review presents a comprehensive taxonomy of MI EEG cross-variability decoding studies from 2020 to 2025, systematically organizing advances in deep learning and transfer learning. We critically evaluate core algorithmic approaches, including Convolutional Neural Networks (CNN), transformers, feature alignment, domain adaptation, and meta-learning. We then explore the underlying mechanisms of these methods and assess their efficacy across key variability paradigms (mainly cross-subject and cross-session scenarios). Finally, we summarize key findings, highlight unresolved challenges, and outline promising future research directions. These advancements hold significant potential to bridge the gap between laboratory-based MI and real-world clinical and consumer applications.
Although physical activity is consistently associated with lower risks of cognitive decline, dementia, and Parkinson’s disease, most exercise research in neurodegeneration still defines exposure by dose-based variables such as duration, intensity, frequency, step count, and cardiorespiratory fitness. This framework is useful for public-health guidance but biologically incomplete for progressive proteinopathies, in which the temporal accumulation and dissemination of misfolded proteins are central to disease evolution. In this review, we propose a temporal intervention framework in which exercise timing is treated as a mechanistic variable that may influence neurodegenerative biology through glymphatic-meningeal lymphatic clearance, vascular pulsatility, circadian alignment, and sleep architecture. We focus on Alzheimer’s disease and Parkinson’s disease because tau and α-synuclein propagation are disease-relevant processes that may be most amenable to intervention during preclinical or prodromal stages. Morning exercise may act primarily through circadian entrainment, afternoon exercise through vascular-metabolic stimulation, appropriately timed evening or low-intensity activity through sleep-related pathways, and sedentary fragmentation through effects on daytime cerebrovascular dynamics and rest-activity rhythm stability. Although the glymphatic-meningeal lymphatic axis provides a plausible link between timed exercise and extracellular protein handling, current human evidence remains indirect and relies heavily on imaging surrogate markers. Future matched-dose trials should integrate accelerometry, sleep physiology, circadian measures, vascular assessments, neurolymphatic imaging, and disease-specific protein biomarkers, including tau PET, plasma p-tau217, and α-synuclein seed amplification assays. Timed exercise should therefore be viewed as a testable biological perturbation that may clarify whether movement timing influences protein propagation in early neurodegenerative disease, not as a proven disease-modifying treatment.
The enteric nervous system (ENS), a central component of the gut-brain axis, is increasingly recognized as an important site in the pathogenesis of neurodegenerative diseases. In Parkinson’s disease (PD) in particular, gastrointestinal dysfunction and pathological changes within the ENS often precede symptoms in the central nervous system (CNS). According to the Braak hypothesis, disease-associated pathology may originate in the gastrointestinal tract and spread to the brain via neural pathways such as the 10th cranial nerve, the vagus nerve. Consequently, the ENS is considered a potential early site of neurodegenerative processes. Despite this growing interest, most studies investigating ENS pathology rely on fluorescence-based approaches that primarily provide information on protein expression and cellular distribution. While these techniques have advanced our understanding of enteric neuronal networks, they offer limited insight into subcellular organization. Ultrastructural analysis of the CNS has already identified characteristic changes at the subcellular level, including morphological alterations of synapses, changes in the endolysosomal system, and mitochondrial dysfunction. Mitochondrial dysfunction represents a key mechanism in PD, as demonstrated in toxin models such as rotenone exposure and in genetic forms involving mutations in PINK1 or PRKN. However, comparable ultrastructural investigations of the ENS remain scarce. This review highlights the importance of ultrastructural investigations of the ENS for understanding early neurodegenerative processes and discusses how electron microscopy (EM) may reveal previously underappreciated cellular and subcellular alterations in enteric neurons. Particular attention is given to mitochondrial pathology and the need for systematic ultrastructural analyses of the ENS in models and human studies of PD.
Though an eminent performance of Artificial Intelligence (AI) is expected to be applied in various technological fields, its high energy consumption is still an issue to be solved for the sustainable implementation of AI into our society. The analog implemented neural networks are promising alternative computation devices with their high-speed convergence and low energy consumption. In this paper, we applied a circuit implemented ReLU Hopfield Neural Network to a mathematical problem with an inequality constraint. After confirming the correspondence between the system dynamics and the search algorithm, we implemented a circuit and observed converged neural circuit outputs that corresponded well to theoretical results and simulations. The objective function value obtained by the proposed analog circuit was within 1.1% relative error of the optimal value.
BackgroundCoagulopathy is a common risk factor in the occurrence of intraventricular hemorrhage (IVH) and subsequent poor neurological outcomes in preterm infants. The causal effect of fresh-frozen plasma (FFP) transfusion and IVH needed to be identified urgently.MethodsWithin the framework of target trial emulation (TTE), we mimic the process of a randomized controlled trial based on retrospective neonatal clinical data collected at Daping Hospital from 2015 to 2025, ultimately including 748 preterm infants with gestational age ≤34 weeks and prolonged activated partial thromboplastin time ≥70 s. Multiple causal inference models were conducted to minimize confounding and covariate imbalance, including inverse probability weighting with generalized boosted models (GBM-IPW) method, GBM-doubly robust (GBM-DR) estimation, covariate balancing propensity score (CBPS) model and other sensitivity analyses. Causal forests algorithm further triangulated the main results and explore individual-level heterogeneity and key determinants about FFP.ResultsAll causal models consistently demonstrated that FFP transfusion exerted no overall estimated protective effect against IVH (all odds ratio (OR) ≈ 1; p > 0.4). GA was found a protective factor of IVH and modified the effect of FFP transfusion (p < 0.05). According the cutoff of 32.3-week cutoff, FFP transfusion significantly increased IVH risk in the more premature subgroup (OR = 2.16, p = 0.02). Causal forests also found individual heterogeneity exists between FFP and IVH, and relevant high-risk subgroups about FFP transfusion can be stratified by prematurity, Apgar scores, and coagulopathy.ConclusionRoutine FFP transfusion is not recommended for IVH prevention in preterm infants. FFP significantly increases IVH risk in more premature infants, while the adverse association of FFP attenuated with increasing GA.
BackgroundPrimary brainstem hemorrhage (PBH) is a severe, poorly-prognostic stroke subtype. Since single biomarkers cannot reflect global inflammatory status, we constructed a composite Inflammatory Burden Index (IBI) incorporating neutrophil-to-lymphocyte ratio (NLR), C-reactive protein-to-albumin ratio (CAR), and systemic immune-inflammation index (SII), and assessed its links with 90-day poor functional outcome and stroke-associated pneumonia (SAP) in PBH patients.MethodsThis retrospective cohort included 347 PBH patients (2015–2023). The IBI was calculated as 0.50 × NLRz + 0.30 × CARz + 0.20 × SIIz, where the weights were assigned a priori from a qualitative synthesis of prior literature and expert judgment rather than derived from the study data. Patients were stratified into tertiles. Primary outcome was poor 90-day functional outcome (modified Rankin Scale 3–6). Secondary outcomes were 90-day mortality and SAP. Multivariable logistic regression, restricted cubic spline analysis, and decision curve analysis were performed. Incremental value over a reference clinical model (age, Glasgow Coma Scale, hematoma volume, intraventricular hemorrhage) was assessed via DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsThe overall rates of poor outcome, mortality, and SAP were 30.3, 12.1, and 16.7%, respectively. Higher IBI tertiles were associated with progressively higher rates of poor functional outcome (18.1, 27.8, and 44.8%; p < 0.001), with graded but non-significant trends for SAP and mortality. In multivariable analysis, IBI independently predicted poor outcome (OR 2.31, 95% CI 1.64–3.26, p < 0.001) and SAP (OR 1.79, 95% CI 1.23–2.59, p = 0.002). RCS confirmed a monotonic dose–response relationship for poor outcome. IBI had the best AUC for poor outcome (0.663), outperforming SII and CAR, though not significantly better than NLR. Adding IBI to the clinical model improved AUC from 0.623 to 0.701 (DeLong p = 0.008; NRI 0.31, IDI 0.045). For SAP, NLR alone was marginally superior (AUC 0.615 vs. 0.614). Decision curve analysis showed net clinical benefit for the IBI model.ConclusionIBI independently predicts poor functional outcome and adds incremental value over clinical factors and most individual markers. However, its utility for SAP does not exceed that of NLR alone; hence IBI should be used selectively for functional prognostication, while NLR remains the preferred biomarker for SAP risk assessment, pending further external validation.
ObjectiveThis study was conducted to assess the relationship between the severity of obstructive sleep apnea (OSA) and the total burden score of cerebral small vessel disease (CSVD), global cerebral blood flow (CBF), and cognitive function. Moreover, the internal pathway through which OSA induces cognitive impairment by affecting cerebral perfusion, and overall cerebral small vessel lesions was determined in this study.MethodsIn total, 94 patients who received polysomnography (PSG) at the Mental Health Center of Inner Mongolia Autonomous Region from October 2024 to February 2026 were included in this study. Based on the apnea-hypopnea index (AHI), all individuals were classified into the control and the mild group (AHI < 15 times/h, n = 26), the moderate OSA group (15 ≤ AHI < 30 times/h, n = 27), and the severe OSA group (AHI ≥ 30 times/h, n = 41). Demographic information was collected from all patients, and their cognitive performance was evaluated using the Mini-Mental State Examination (MMSE), Trail Making Test (TMT), Choice Reaction Time task (CRT), Digit Symbol Substitution Task (DSST), and the Trails test with a paradigm similar to Part B of the Trail Making Test. Each patient underwent 3.0T magnetic resonance imaging, including conventional sequences, susceptibility weighted imaging (SWI), and arterial spin labeling (ASL). Finally, the total CSVD burden score was assessed.ResultsThe total CSVD burden and perivascular space (PVS) score in the severe OSA group were significantly higher than those in the moderate group and the control group (P < 0.05). The total CSVD burden was significantly positively correlated with the mean global CBF (P < 0.05). After the BMI was adjusted, the severity of OSA remained independently associated with the mean global CBF (P < 0.05). Total CSVD burden, PVS, lacunes, and global CBF were significantly associated with cognitive function domains (P < 0.05), which indicated that CSVD and global CBF abnormalities may jointly participate in OSA-related cognitive function.ConclusionThe severity of OSA might be related to the CSVD burden score. Neuroimaging characteristics strongly related to cognitive function in patients suffering from OSA, and abnormal global CBF may serve as a non-invasive imaging marker for evaluating neurocognitive damage.
PurposeThis study investigated the topological organization of brain networks in Sanda athletes across different training levels to elucidate the neural mechanisms underlying the sport’s exceptional sensorimotor integration and training-related neural plasticity.MethodsMultimodal magnetic resonance imaging (MRI) data were collected from 35 participants, including elite athletes, second-level athletes, and novices. Using the Automated Anatomical Labeling 116-region atlas (AAL116), structural connectivity (SC) networks were constructed based on fractional anisotropy (FA) derived from diffusion tensor imaging, while functional connectivity (FC) networks were computed using Pearson correlations between regional blood-oxygen-level-dependent (BOLD) time series. Structure–function coupling (SFC) networks were generated as a weighted linear combination of SC and FC. Network differences were assessed using Network-Based Statistic (NBS) and graph-theoretical analyses.ResultsElite athletes exhibited prominent structure–function coupling within visual–motor–executive pathways, whereas novices showed greater involvement of language–rhythm-related pathways. At the global level, elite athletes demonstrated stronger small-world organization and network synchrony, suggesting more efficient information transmission. At the nodal level, the parietal–cerebellar system emerged as a critical hub across the examined networks, with particularly prominent involvement of the right supramarginal gyrus (SMG.R) and right cerebellar lobule VI (CRBL6.R).DiscussionThese regions are likely to play a central role in sensorimotor integration and rhythm control. These findings confirm that long-term Sanda training optimizes brain network topology, providing key insights into the neural plasticity of athletic expertise.
