搜索结果：Frontiers in neuroscience

Chronic pain (CP) is a public health challenge recognized as involving large-scale functional brain dysregulation. Acupuncture is widely used as a non-pharmacological intervention for CP, yet its central mechanisms remain incompletely understood. fMRI provides an approach for investigating acupuncture-related brain alterations in CP. Eight databases were searched from inception to March 27, 2025 for fMRI studies investigating acupuncture's central effects in CP. Eligible studies included randomized controlled trials and observational studies involving migraine, knee osteoarthritis, fibromyalgia, sciatica, chronic shoulder pain, chronic neck pain, cervical spondylosis, chronic low back pain, and lumbar disk herniation. Data on characteristics, acupuncture protocols, neuroimaging findings, and outcomes were extracted and narratively synthesized. Reporting quality of acupuncture interventions was assessed using STRICTA, risk of bias of randomized controlled trials using RoB 2, and methodological quality of observational studies using the Newcastle-Ottawa Scale. A total of 64 studies were included. CP was characterized by widespread functional brain abnormalities, mainly involving the default mode network, sensorimotor network, and pain- and emotion-related regions such as the anterior cingulate cortex, precuneus, insula, and thalamus. Across longitudinal and controlled analyses, acupuncture-related brain changes were most consistently reflected in altered functional connectivity, local neural synchrony, and regional spontaneous activity. Functional connectivity findings suggested a potentially ACC-centered circuit pattern, whereas regional homogeneity findings revealed bidirectional modulation across multiple brain regions. Comparative evidence further indicated that VA, SA, and EEA may engage partially overlapping but distinct neural processes. Reporting of core acupuncture protocol components was generally adequate, whereas methodological quality remained heterogeneous. Current fMRI evidence suggests that CP involves large-scale network-level functional imbalance and that acupuncture may be associated with modulation of key abnormal nodes and circuits related to pain perception, sensory processing, and emotional regulation. The available evidence supports a cautious interpretation that acupuncture-related brain effects may predominantly reflect a state-dependent recalibration of dysregulated brain networks. Future studies should prioritize large-sample, multicenter, longitudinal, and multimodal designs, together with rigorous control settings and more rigorous, externally validated machine learning-based prediction studies, to better distinguish differential central effects across intervention conditions and advance mechanism-informed personalized acupuncture in CP management.

Recent transcriptome analysis has demonstrated increased expression of Vascular Endothelial Growth Factor receptor-1 (VEGFR-1/FLT1) and in AD brain. Increased expression of VEGFR1 and its ligand VEGFB were associated with a more rapid rate of cognitive decline, providing evidence of a potential link between increased VEGFR-1 expression in AD pathogenesis. In this study, we explored the potential role of VEGFR-1 expression in neurons on AD pathology. To confirm VEGFR1 expression in AD brains, we first performed immunostaining in AD brain sections (AD - Braak stage V-VI, and normal controls - Braak 0-II). And to determine a potential detrimental role of neuronal VEGFR1 expression on AD associated pathologies, we exposed SH-SY5Y human neuroblastoma cells and mouse primary neurons to either hypoxia conditions (1%O2) or 5 μ Aβ1-42 oligomers or fibrils for 24, 28 and 72hrs. In this study, we found preferential staining of VEGFR-1 in the neuropil and neuronal cell bodies both in AD and Control hippocampus and increased VEGFR-1 immunoreactivity in dystrophic neuritic processes in the vicinity of Thio-S positive amyloid plaques in AD brains. And treatment of SH-SY5Y human neuroblastoma cell line and mouse primary neurons, with either hypoxia conditions or Aβ1-42 oligomers, resulted in increased VEGFR-1 expression and cleaved caspase 3 activation, leading to neuronal toxicities/cell death. Similarly, treatment with Aβ1-42 fibrils also increased VEGFR-1 and cleaved caspase 3 protein levels in the SH-SY5Y cells whereas treatment with Aβ1-42 monomers had no effect on VEGFR-1 expression. In addition, we show that over-expression of VEGFR-1 intracellular domains in SH-SY5Y cells directly induced neuronal toxicities and importantly, siRNA-mediated knockdown of VEGFR-1 in neurons prevented the hypoxia, Aβ1-42 oligomer and Aβ1-42 fibril-induced toxicities and cell death phenotypes. Treatment with either hypoxia or Aβ1-42 oligomers also reduced expression of cell survival genes including VEGFR-2 and Hippo pathway YAP1 and siRNA-mediated VEGFR-1 knockdown in the neurons normalized expression of both VEGFR-2 and YAP1. Using differential gene expression analysis, we demonstrated upregulation of several inflammatory/interferon-stimulated genes (ISGs) as well as increased expression of genes involved in activation of oxidative stress and cell death pathways in response to Aβ1-42 oligomers treatment in mouse primary neurons. And siRNA-mediated VEGFR-1 knockdown in the mouse primary neurons, reduced gene expression of both the ISGs and oxidative stress/cell death pathways in response to Aβ1-42 oligomer treatment. In summary, these results show that siRNA-mediated knockdown of VEGFR-1 in neurons significantly prevented hypoxia, Aβ1-42 oligomer and Aβ1-42 fibril-induced cellular toxicities and cell death phenotypes, indicating a potential detrimental role of aberrant VEGFR-1 expression and signaling in response to AD associated pathologies.

Motor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction. To tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain-class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment. Extensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users. To the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating cerebrovascular disease associated with high rates of mortality and long-term disability. Early risk stratification is essential to guide personalized management. Systemic inflammation plays a key role in secondary brain injury after aSAH. The systemic inflammation response index (SIRI), a novel inflammatory marker combining neutrophil, monocyte, and lymphocyte counts, has shown prognostic value in multiple disorders, but its long-term prognostic role in aSAH remains unclear. This study aimed to investigate the association between admission SIRI and 12-month unfavorable functional outcomes (modified Rankin Scale [mRS]&#x202f;&#x2265;&#x202f;3) in patients with aSAH, verify its independent prognostic value, and construct a clinically useful prediction nomogram. A retrospective cohort study was performed including 258 patients with aSAH admitted between January 2021 and December 2024. Patients were divided into a favorable prognosis group (mRS 0-2, n&#x202f;=&#x202f;158) and an unfavorable prognosis group (mRS&#x202f;&#x2265;&#x202f;3, n&#x202f;=&#x202f;100). Baseline characteristics, imaging indices including modified Fisher scale, laboratory parameters, and treatment data were collected. Multivariate logistic regression with forced entry was used to identify independent prognostic factors. Restricted cubic spline (RCS) analysis was applied to explore the non-linear relationship between SIRI and prognosis. A prediction nomogram was constructed and validated using temporal validation (training cohort n&#x202f;=&#x202f;170; validation cohort n&#x202f;=&#x202f;88). Model performance was evaluated using discrimination, calibration, and decision curve analysis. SIRI was significantly higher in the unfavorable prognosis group (p&#x202f;<&#x202f;0.001). Multivariate analysis confirmed that SIRI (OR&#x202f;=&#x202f;1.20, 95% CI: 1.08-1.34, p&#x202f;=&#x202f;0.001), age, hypertension, GCS score &#x2264; 8, modified Fisher scale, and treatment modality were independent prognostic factors. RCS analysis demonstrated a non-linear relationship (P for nonlinearity&#x202f;=&#x202f;0.020), with a clear threshold at SIRI&#x202f;=&#x202f;4.36; the risk of unfavorable outcomes rose steeply above this cutoff. The nomogram showed excellent discrimination (AUC&#x202f;=&#x202f;0.881 in training; 0.919 in validation) and satisfactory calibration. Decision curve analysis confirmed favorable clinical utility. Admission SIRI is an independent predictor of 12-month unfavorable functional outcomes in patients with aSAH. A threshold value of 4.36 can effectively identify high-risk patients. The SIRI-integrated nomogram provides accurate and individualized prognosis prediction across both training and temporal validation cohorts. This validated tool provides robust evidence to support clinical risk stratification and personalized decision-making.

Pattern visual evoked potentials (pattern VEP) are widely used for functional assessment of the visual pathways. The P100 component represents the principal clinical parameter owing to its relative interindividual stability and diagnostic value. However, both latency and amplitude are modulated by multiple physiological and environmental factors, which complicates interpretation and the establishment of reliable reference standards. This scoping review aimed to systematically map determinants of P100 parameters in healthy individuals. The review was conducted in accordance with PRISMA-ScR and Joanna Briggs Institute methodology. Databases were searched for studies published between 2015 and 2025 that examined biological, refractive, anthropometric, metabolic, or environmental influences on pattern VEP parameters in healthy populations. Owing to methodological heterogeneity, findings were synthesized descriptively. Thirty-nine studies met the inclusion criteria. Age emerged as the most consistent determinant of P100 parameters. Latency followed a non-linear trajectory across the lifespan, with shortening during maturation, stabilization in early adulthood, and progressive prolongation after approximately 40&#x202f;years of age, whereas amplitude generally declined with aging. Sex differences predominantly affected amplitude, with women typically demonstrating higher P100 or N75-P100 amplitudes in adult populations; latency differences were less consistent and often minimal in paediatric cohorts. Retinal image quality exerted a strong dose-dependent effect on P100 parameters: increasing refractive blur and higher-order aberrations were associated with progressive latency prolongation and amplitude reduction, particularly for small check sizes. Ocular dominance showed no clinically meaningful interocular asymmetry. Metabolic disturbances were associated with prolonged latency in selected populations, whereas anthropometric variables such as head size and height demonstrated weak or inconsistent associations. Among environmental factors, acute alcohol intake prolonged P100 latency, while moderate caffeine consumption had no significant effect. Age and retinal image quality represent the primary physiological determinants of P100 latency and amplitude in healthy individuals. Most other modifiers exert modest or context-dependent effects. Consideration of these variables is essential for accurate interpretation of pattern VEP recordings and for establishing reliable local reference standards consistent with ISCEV recommendations.

Microglia, the resident immune cells of the central nervous system, play a critical role in maintaining neural homeostasis and regulating inflammatory responses in the brain. Increasing evidence suggests that microglial dysfunction contributes to the progression of neurodegenerative diseases, including Alzheimer's disease (AD). However, the molecular mechanisms underlying these alterations remain incompletely understood. This study aimed to characterize disease-associated molecular changes in microglia derived from induced pluripotent stem cells (iPSCs) of sporadic AD patients and healthy donors. iPSC-derived microglia from sporadic AD patients and healthy controls were analyzed using integrated multi-omics approaches, including total RNA sequencing, proteomics, and small non-coding RNA (sncRNA) sequencing. Gene Ontology (GO) analysis was performed to identify dysregulated biological pathways from transcriptomic and proteomic datasets. In addition, a modified T4 polynucleotide kinase (T4 PNK)-based sncRNA sequencing method was used to profile disease-associated sncRNAs and identify previously uncharacterized RNA species. Comparative analyses revealed significant AD-associated alterations in mRNA, protein, and sncRNA expression profiles in iPSC-derived microglia. GO analysis demonstrated dysregulation of pathways related to extracellular communication, intracellular transport, cytoskeletal organization, and protein-protein interactions. Furthermore, the modified T4 PNK-sncRNA sequencing approach identified multiple disease-associated sncRNAs, including several novel and previously uncharacterized RNA species potentially linked to AD pathology. These findings demonstrate that iPSC-derived microglia provide a valuable model for studying molecular mechanisms associated with sporadic AD. The identified transcriptomic, proteomic, and sncRNA alterations highlight key pathways potentially involved in microglial dysfunction and neurodegeneration. In particular, the discovery of novel disease-associated sncRNAs may provide new insights into AD pathogenesis and reveal potential therapeutic targets for future investigation.

Timely identification of individuals at risk for Alzheimer's disease (AD) progression remains a major clinical challenge. Traditional cognitive assessments provide limited prognostic insight, while many machine learning (ML) models rely on costly biomarkers or poorly interpretable algorithms that limit clinical scalability. This study evaluated whether widely available baseline demographic, clinical, and cognitive measures could support short-term progression prediction using interpretable ML methods under extreme class imbalance. We analyzed 3,240 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of whom 2,423 had valid 24-month follow-up data. The primary outcome was strict unidirectional diagnostic worsening within 24&#x202f;months (13 events; 0.5%). Baseline demographic, clinical, and cognitive variables were used to train XGBoost and logistic regression models under natural class imbalance using stratified k-fold cross-validation with out-of-fold predictions. Model performance was evaluated using AUROC, area under the precision-recall curve (AUPRC), calibration analyses, and bootstrap confidence intervals. Sensitivity analyses evaluated cost-sensitive learning, threshold optimization, and alternative imputation strategies (KNN and MICE). Longitudinal mixed-effects modeling was conducted separately to characterize cognitive decline and was not used as input to the predictive models. SHAP (Shapley Additive Explanations) quantified feature contributions. Under natural class imbalance, XGBoost achieved AUROC&#x202f;=&#x202f;0.912 and AUPRC&#x202f;=&#x202f;0.051, while logistic regression achieved AUROC&#x202f;=&#x202f;0.787 and AUPRC&#x202f;=&#x202f;0.038. Although discrimination exceeded baseline prevalence, precision remained low and threshold optimization produced substantial false-positive burdens, limiting immediate clinical applicability. Cost-sensitive learning did not materially improve performance. MICE imputation produced results comparable to median imputation, whereas KNN imputation reduced performance. SHAP analyses identified baseline cognitive severity, functional measures, and diagnostic status as dominant predictors. Mixed-effects modeling confirmed significant cognitive decline over time (&#x3b2; =&#x202f;-0.027 points/month, p <&#x202f;0.001). Accessible baseline clinical and cognitive variables contain measurable but limited predictive signal for short-term AD progression under extreme event scarcity. These findings should be interpreted as an early-stage proof-of-concept rather than a clinically deployable decision-support tool. External validation remains necessary before clinical translation.

Although we have an increasingly sure grasp of much of its proximate circuitry, we continue to lack the ultimate physiological rationale of rapid eye movement (REM) sleep. In this paper, we propose that REM sleep derives both its proximate mechanisms as well as its ultimate cause from photoperiodism. (This refers to the means whereby many organisms translate information about day length into appropriately timed physiological adjustments ensuring their survival through the most challenging season-usually winter). First, the REM sleep interval serves, we suggest, as a sampling device attuned to a particular species of sidereal signal that materializes only in the crepuscular intervals of the day (when light slowly changes place with darkness) and that becomes fully available to the animal only in the shorter days (SDs) of the year. Second, REM sleep serves as an interval timer sensitive to the duration between shorter vs. longer phasic REM episodes, a distinction which a defined set of astrocytes then translates into that between, respectively, a temporal interval incapable of supporting aerobic glycolysis (AG) vs. one fully capable of doing so. Accordingly lactate, the product of AG, functions as a SD-specific signal triggering a behavioral, metabolic, and neuroprotective/neurogenetic program allowing the animal to survive winter. Outlined is the CNS seasonal module responsible for recognizing the lactate signal and disseminating it through the seasonal animal. This includes a novel photoperiodic role for the central extended amygdala and in particular the bed nucleus of the stria terminalis (BNST). Our model clarifies many different aspects of the REM sleep/seasonal amalgam including its coopting of basic arousal circuitry so as to support behavioral bistability, a key feature of the photoperiodic organism. Thus a remarkable but heretofore poorly understood phenomenon, a phase of hyperarousal preceding the descent into involution, falls into place as part of the strategy for surviving winter. Finally, our hypothesis is concordant with recent evidence demonstrating that the gene set subserving so-called lactate-mediated neural plasticity emerged well before that supporting traditional (explicit) memory, a specialty of mammals and their hippocampal tissue.

Most studies on social exclusion adopt virtual paradigms focusing on unilateral responses, while neglecting real-world face-to-face interaction and its neural basis. Functional near-infrared spectroscopy (fNIRS) hyperscanning allows recording of interpersonal neural synchronization (INS) during dyadic interaction, providing a novel approach for investigating interpersonal cooperation. This study recruited 24 dyads of college students randomly assigned to social exclusion and inclusion groups. Using fNIRS hyperscanning combined with a face-to-face rejection paradigm and the Prisoner's Dilemma task, we examined subjective experience, behavior, and INS. (1) The exclusion group reported lower intimacy, trust, belonging need and state self-esteem than the inclusion group. (2) Only defection decision reaction time was faster in the exclusion group, with no group differences in overall cooperation and defection rates, indicating exclusion primarily accelerates defection. (3) INS showed channel-specific differences: the exclusion group had weaker right orbitofrontal INS during cooperation and stronger left dorsolateral prefrontal INS during defection. (4) Cooperation reaction time negatively correlated with trust, while defection efficiency positively correlated with left frontopolar INS. (5) State self-esteem partially mediated the link between social exclusion and defection reaction time. From an integrated psychological-behavioral-neural perspective, this study confirms that face-to-face social exclusion accelerates defection decisions by impairing subjective interpersonal experience, altering prefrontal INS, and through the mediating effect of subjective feelings. These findings provide empirical evidence for understanding the mechanisms underlying campus social exclusion.

GABAergic interneurons (IN) are critical for the precise timing and flow of information in cortical circuits. Loss of GABAergic IN function has been suggested as a potential translationally relevant mechanism of neuropathology in Fragile X Syndrome (FXS). Indeed, in rodent models of FXS, some IN populations may display reduced number, while genes associated with other IN type upregulated. However, it remains unknown how these cell populations, and their cell-type specific gene expression patterns are regulated in early development across other mammalian models of FXS. Here we utilise an outbred rat model of FXS, in which we have performed single-nucleus RNA sequencing analysis in neonatal development of the somatosensory cortex. We then use immunohistochemistry to measure the number and distribution of neurochemically identified GABAergic IN subtypes from early development until adolescence in the somatosensory cortex. We find that GABAergic INs in a rat model of FXS display clear evidence of transcriptomic alteration compared to wild-type littermates in early brain development. These effects are most profound in putative parvalbumin INs, but with modest changes in other cell types. From immunohistochemistry, we find that parvalbumin INs appear largely unaffected in density in distribution, but we observe a large upregulation in the number of somatostatin-expressing INs. GABAergic INs may display cell-type specific transcriptomic regulation in response to the loss of FMRP. Our data suggests minimal alteration of parvalbumin IN density or distribution, but upregulated somatostatin cell numbers. These data partially agree with previous observations in mouse models of FXS.

Cervical spinal cord injury (SCI) results in severe upper limb impairment, with restoration of hand and arm function ranked as the highest rehabilitation priority by individuals with tetraplegia. Transcutaneous spinal cord stimulation (tSCS) has emerged as a promising approach for enhancing upper limb recovery. Intermittent theta burst stimulation (iTBS), an efficient form of repetitive transcranial magnetic stimulation, can enhance cortical excitability and descending motor drive. However, the benefit of combining these complementary neuromodulation modalities to simultaneously target supraspinal and spinal circuits has not been evaluated in a controlled trial. This study aims to evaluate the feasibility, safety, and preliminary efficacy of combined cortical neuromodulation (iTBS) and spinal neuromodulation (tSCS) versus tSCS alone, each paired with standardized upper limb rehabilitation, for improving upper limb motor function in chronic incomplete cervical SCI. This single-center, two-arm, assessor-blinded, pilot randomized controlled trial will enroll 24 adults aged 21-65 years with chronic (more than 12 months post-injury) incomplete cervical SCI (American Spinal Injury Association Impairment Scale grade C or D, neurological level C2 to C8) at Alexandra Hospital, Singapore. Participants will be randomized 1:1 to receive either iTBS combined with tSCS plus standardized upper limb rehabilitation or tSCS plus upper limb rehabilitation alone. Interventions will be delivered twice weekly for 12 weeks (24 sessions). The primary outcome is the change in Upper Extremity Motor Score from baseline to 12 weeks. Secondary outcomes include measures of upper limb function, independence, spasticity, corticospinal excitability, quality of life, and goal attainment. Assessments will be conducted at baseline, post-intervention, and at 4- and 12-week follow-up. This protocol was approved by the National Healthcare Group Domain Specific Review Board. Recruitment is expected to begin in the third quarter of 2026, with data collection anticipated to be completed by the fourth quarter of 2027. This pilot trial will provide the first controlled evidence on whether adjunctive cortical neuromodulation via iTBS produces additional upper limb motor recovery beyond tSCS-based rehabilitation in chronic incomplete cervical SCI. Feasibility data and effect size estimates will inform the design of a subsequent multicenter confirmatory trial. ClinicalTrials.gov, identifier NCT07586644.

Patients with glioma are at high risk of postoperative venous thromboembolism (VTE) and postoperative neurological deterioration (PND). Conventional clinical scoring systems have limited accuracy in predicting these perioperative risks. This study aimed to develop and validate machine-learning models for individualized preoperative prediction of postoperative VTE and PND in patients with glioma. A retrospective cohort of 427 patients with glioma was included. Patients were randomly divided into training and test sets at an 8:2 ratio using stratified random sampling. Multiple machine-learning algorithms were trained and evaluated. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis. An online prediction platform was developed to facilitate individualized risk assessment. Among 427 patients, postoperative VTE and PND occurred in 34 and 35%, respectively. For VTE prediction, the final Top-10 random forest model outperformed the Caprini score alone and achieved an AUC of 0.815 (95% CI, 0.720-0.910) in the held-out test set. Performance remained strong in the clinically significant VTE sensitivity analysis (AUC, 0.923; 95% CI, 0.847-0.998). SHAP analysis indicated that older age, elevated D-dimer and fibrin degradation products (FDP), as well as lower hemoglobin levels, were associated with increased predicted VTE risk. For PND prediction, the final Top-10 logistic regression model achieved an AUC of 0.741 (95% CI, 0.627-0.854). Older age, recurrent glioma, higher Caprini score, higher neutrophil percentage, and hypertension history tended to increase predicted PND risk. Models were deployed in the GLOBE web platform (https://gliomas.shinyapps.io/GLOBE/) for real-time preoperative risk prediction. We developed accurate, interpretable, and clinically meaningful preoperative prediction models for postoperative VTE and PND in patients with glioma. The GLOBE online prediction system translates these models into a practical tool for individualized perioperative risk stratification.

Advanced Alzheimer's disease (AD) is generally regarded as a stage of irreversible functional decline. Psilocybin is known to transiently alter large-scale brain network dynamics and to induce plasticity-related mechanisms in preclinical models, yet clinical data in advanced dementia remain lacking. We report the case of an octogenarian Japanese-American woman with a 10-year history of Alzheimer's disease, including 5&#x202f;years of marked hypofunction and predominantly monosyllabic speech. Baseline features included chronic urinary incontinence, executive dysfunction, dysphagia, dependent mobility, flat affect, and severe reduction in spontaneous communication. The patient received 5 g of orally administered psilocybin-containing mushrooms (Enigma strain). The acute phase was marked by autonomic activation, clinically suspected hyperthermia, profuse sweating, and a prolonged deep sleep-like state. Approximately 19&#x202f;h post-administration, spontaneous autobiographical speech emerged. Over subsequent days and weeks, functional improvements included restoration of urinary continence, improved ambulation, autonomous dressing, increased emotional responsiveness, sustained social interaction, contextual memory retrieval, preserved working memory for social context, and spontaneous conversational engagement. This case documents transient multidomain functional improvement in advanced Alzheimer's disease following psilocybin administration. The findings do not imply disease reversal but suggest that residual functional capacity may persist in late-stage neurodegeneration and may become transiently accessible under specific neuromodulatory conditions.

The neural encoding of voicing in speech sounds has been relatively well studied in monolingual, native speakers. Little research, however, has examined neural encoding of aspiration feature in speech sounds or focused on encoding of non-native phonetic features or the effect of noise on processing these features. This study examined Auditory Evoked Potentials (AEPs) to bilabial stops with English versus Hindi phonetic properties of aspiration and voicing Hindi, English and Tamil listeners, in quiet and in noise. A total of 48 participants (16 Hindi, 16 American English, and 16 native Tamil native speakers) between 20 and 45 years of age participated. Natural digitized speech sounds including Hindi /ba/, /pa/, and /pha/ and American English /ba/, and /pa/ were presented at 70&#x202f;dB SPL using insert earphones in quiet, and in background noise at signal-to-noise ratio of 0. AEP peaks P1, N1, P2, and N2 were measured at the central electrode site (FCz). The P1 and P2 peak amplitudes were significantly larger for Hindi CV stimuli in Hindi participants relative to American English participants and Tamil participants. The morphology to Hindi /pha/ was similar to English /pa/, and the morphology for Hindi /pa/ was similar to English /ba/. P1 amplitudes were larger and P2 amplitudes were smaller in noise relative to quiet. N2 peak latency in response to Hindi /pa/ was slightly longer relative to Hindi /pha/ in American English listeners. The findings add evidence to the Automatic Speech Perception model by observing cross-linguistic differences in P1 and P2. The results contribute to a better understanding of neural encoding in the cortex across native and non-native listeners, and how noise modulates early stages of processing.

To analyze quantitative electroencephalographic (EEG) characteristics during Motor Imagery Brain-Computer Interface (MI-BCI) task in patients with prolonged disorders of consciousness (pDoC). Forty-three patients with pDoC due to various brain injuries were enrolled. Based on modified Coma Recovery Scale-Revised (CRS-R) assessments, the patients were divided into 19 in the unresponsive wakefulness syndrome (UWS) group and 24 in the minimally conscious state (MCS) group. All patients underwent 5 min of resting-state (RS) EEG followed by 5 min of MI-BCI task. Relative power, DTABR, and average brain engagement (BE) during MI-BCI were analyzed across resting and MI-BCI states using Fast Fourier Transform (FFT) spectra. Mixed-design ANOVA showed significant main effects of condition and group across all EEG frequency bands, indicating clear differences between the RS and MI-BCI conditions and between UWS and MCS patients. Significant group &#xd7; condition interactions were found in the delta, beta, and gamma bands, as well as in DTABR. Simple effects analysis showed that delta power was higher in RS than in MI-BCI in both groups, with UWS consistently exhibiting higher delta power than MCS under both conditions. In contrast, beta and gamma power were higher in MI-BCI than in RS in both groups. For beta power, UWS was higher than MCS under RS, whereas MCS was higher than UWS under MI-BCI, showing a reversal of the interaction pattern. For gamma power, MCS showed higher values than UWS under both conditions, with a larger between-group difference during MI-BCI. DTABR was significantly higher in RS than in MI-BCI in both groups; however, MCS exhibited higher DTABR than UWS under RS, whereas the opposite pattern was observed under MI-BCI. In addition, during MI-BCI tasks, the MCS group showed greater average BE than the UWS group. MI-BCI shows potential as a diagnostic or assessment tool for evaluating the level of consciousness in patients with pDoC.

This study applied multivariate ANOVA to investigate age-related microstructural changes in the brain tissues driven primarily by myelin, iron, and water content, as observed in MRI (semi-)quantitative R1, R2*, MTsat and PD maps. This is effectively a re-analysis of the data analyzed in a univariate way in a previous publication. Voxel-wise analyses were performed on gray matter (GM) and white matter (WM), in addition to region of interest (ROI) analyses. The multivariate approach identified brain regions showing coordinated alterations in multiple tissue properties and demonstrated bidirectional correlations between age and all examined modalities in various brain regions, including the caudate nucleus, putamen, insula, cerebellum, lingual gyri, hippocampus, and olfactory bulb. The multivariate model was more sensitive than univariate analyses, as evidenced by detecting a larger number of significant voxels within clusters in the supplementary motor area, frontal cortex, hippocampus, amygdala, occipital cortex, and cerebellum bilaterally. Though when cross validating the results by splitting the data into 2 subsets, sensitivity is strongly reduced, even more so for the multivariate approach. The examination of normalized, smoothed, and z-transformed maps within the ROIs revealed concurrent age-dependent alterations in myelin, iron, and water content. These findings contribute to our understanding of age-related brain differences and provide insights into the underlying mechanisms of aging. The study emphasizes the importance of multivariate analysis for detecting subtle microstructural changes associated with aging when dealing with multiple quantitative MRI parameter maps.

Neonatal hypoxic-ischemic encephalopathy (HIE) stands as a primary cause of neonatal brain injury, mortality, and long-term neurodevelopmental impairment, imposing a considerable burden on public health systems worldwide. Despite advances in clinical practice, accurate early diagnosis and reliable severity stratification of HIE remain formidable challenges when relying solely on conventional neuroimaging modalities. A cohort of 66 term neonates with clinically confirmed HIE and 25 age-matched healthy controls underwent cranial magnetic resonance imaging (MRI) combined with diffusion kurtosis imaging (DKI) within the first 28&#x202f;days of life. A region-of-interest (ROI)-based quantitative analysis was conducted to quantify kurtosis and diffusion metrics across distinct white matter and deep gray matter regions. The diagnostic utility and severity-stratifying capacity of these DKI parameters were evaluated using receiver operating characteristic (ROC) curve analysis, while their potential correlations with 1-min and 5-min Apgar scores were further explored. ROI-based analysis revealed significant differences in DKI parameters between neonates with HIE and healthy controls across multiple brain regions. Notably, DKI parameters derived from deep gray matter exhibited superior diagnostic performance and severity stratification capacity, with several kurtosis indices achieving an area under the ROC curve (AUC) greater than 0.90. Subgroup comparisons further demonstrated that specific DKI parameters differed significantly between mild and moderate, and between mild and moderate-to-severe HIE cases. Among all measured indices, the mean kurtosis of the corpus callosum yielded relatively high discriminative efficacy. Additionally, a subset of DKI parameters showed significant correlations with Apgar scores, with a greater number of parameters correlating strongly with the 5-min Apgar score. DKI yields robust quantitative biomarkers that facilitate the accurate diagnosis and reliable severity stratification of neonatal HIE, with deep gray matter emerging as a pivotal indicator of brain injury severity. These findings underscore the potential value of advanced diffusion-weighted imaging techniques in enabling early risk stratification and standardized assessment of neonatal brain injury, which carries profound implications for the optimization of public health-oriented neonatal care strategies.

Growing evidence suggests a mechanistic link between type 2 diabetes mellitus and Parkinson's disease (PD), with insulin-degrading enzyme (IDE) implicated in both insulin and amyloid-&#x3b2; metabolism, as well as &#x3b1;-synuclein degradation. However, the role of IDE in PD pathogenesis remains insufficiently defined. This study aimed to investigate the association of IDE gene polymorphisms and serum IDE levels with sporadic PD in a Chinese Han population. Fourteen single nucleotide polymorphisms (SNPs) within the IDE gene were genotyped in 463 patients with sporadic PD and 576 age- and sex-matched healthy controls (HCs). An independent cohort of 100 PD patients and 100 HCs was used to quantify serum IDE concentrations. Correlations between IDE levels and clinical features were assessed. Logistic regression was employed to identify independent factors associated with PD. Among the examined SNPs, rs11187007 showed a nominal allelic association with PD (P = 0.046), which did not survive the Bonferroni correction. Serum IDE concentrations were significantly higher in PD patients than in HCs (P = 0.015). Elevated IDE levels were negatively correlated with Mini-Mental State Examination scores (R = -0.230, P = 0.027) and positively associated with more severe symptoms. Logistic regression indicated that elevated serum IDE levels were associated with PD. Our findings highlight that elevated serum IDE correlates with PD, suggesting a role for IDE in neurodegeneration, warranting further mechanistic and longitudinal studies to evaluate its potential as a therapeutic target in PD.

We present novel evidence that humans are capable of producing, perceiving, and cortically processing ultrasound (US), extending the recognized limits of human auditory function. This previously unacknowledged sensory ability was identified in an expert practitioner of the traditional Chinese health exercise "The Six Healing Sounds. High-resolution recordings demonstrated deliberate vocal emissions reaching up to 35 kHz, with nearly one third of the acoustic energy residing in the ultrasonic range. Magnetoencephalography (MEG) revealed a distinct cortical response to these US components, characterized by an additional early N1 peak at 87 ms in the left auditory cortex-about 20-25 ms earlier than the conventional N1 response. Importantly, this US-specific component was entirely absent in a large cohort of 202 vocalists comprising 23 Chinese and 172 European trained singers and furthermore 7 experienced 'Healing Sound' singers, none of whom produced or perceived vocal US. Beyond cortical physiology, psychophysical testing demonstrated a unique perceptual strategy in the qigong practitioner: at very high frequencies his pitch perception shifted from spectral to fundamental mode, enabling him to reconstruct coherent auditory objects even when ultrasonic components were only weakly present. This finding may reflect an exceptional adaptive neuroplastic mechanism integrating US into auditory and regulatory brain networks, akin to functional reorganizations observed in professional musicians. Taken together, these results provide the first demonstration in a human case study of intentional ultrasonic vocalization combined with cortical US processing in a human. The discovery not only challenges the traditional definition of auditory limits but also opens promising perspectives for the role of US in internal communication, vegetative regulation, and clinical neuromodulation. Low-intensity US stimulation may thus represent a novel therapeutic avenue for auditory and neurological disorders.