Insomnia disorder (ID) is a common sleep-wake disorder characterized by persistent difficulty initiating or maintaining sleep, early-morning awakening, or non-restorative sleep, accompanied by daytime functional impairment. ID has traditionally been explained by the hyperarousal model, which emphasizes cognitive, emotional, cortical, neuroendocrine, and autonomic overactivation. However, this model alone does not fully account for the chronic persistence, relapse tendency, and multisystem associations of ID. Emerging evidence suggests that circadian rhythm disruption, impaired melatonin signaling, hypothalamic-pituitary-adrenal (HPA) axis activation, autonomic imbalance, and low-grade inflammation may also contribute to the development and maintenance of ID. Available evidence indicates that sleep disturbance is more consistently associated with selected inflammatory markers, particularly C-reactive protein (CRP) and interleukin-6 (IL-6), whereas findings for tumor necrosis factor-alpha (TNF-α) remain less consistent. The circadian system regulates sleep, endocrine function, metabolism, and immune-inflammatory activity through the suprachiasmatic nucleus, melatonin and cortisol rhythms, peripheral clock genes, and rhythmic immune-cell responses. Disruption of this temporal network may alter melatonin secretion, inflammatory rhythmicity, and stress-related neuroendocrine responses, thereby contributing to the persistence of insomnia symptoms. Compared with previous reviews that have separately discussed hyperarousal, circadian rhythm disruption, melatonin signaling, or sleep-related inflammation, this review integrates these processes into a circadian-immune perspective for understanding ID. We summarize alterations in sleep-wake rhythms, melatonin signaling, HPA-axis activity, autonomic regulation, and immune-inflammatory responses in ID, and discuss potential intervention strategies, including light management, melatonin and melatonin receptor agonists, cognitive behavioral therapy for insomnia (CBT-I), physical activity, time-restricted eating, and stress management. This review aims to provide a mechanistic basis for understanding the chronicity and heterogeneity of ID and for developing individualized intervention strategies.
Freezing of gait (FOG) is one of the most debilitating motor symptoms in Parkinson's disease (PD), affecting a substantial proportion of people with PD. Numerous hypothetical mechanisms exist, attempting to explain FOG. Current evidence originated from stationary measures in lying positions, limiting the understanding of neurophysiological changes elicited by FOG episodes during regular locomotion. Neurophysiological aspects of FOG, especially in response to vibrotactile cueing, have been explored only by a few studies. To address this gap, the current study aims to investigate FOG episodes in people with PD and the impact of vibrotactile cueing while walking through virtual environments on a self-paced treadmill. Thirty people with Parkinson's Disease and FOG will be recruited. They will undergo screening and familiarization before performing different walking tests in virtual reality using the Gait Real-time Analysis Interactive Lab (GRAIL). Participants will perform two experimental blocks, each consisting of four walking trials of approximately 10 min. A break of 10 to 30 min will be provided between blocks. Walking with or without vibrotactile cueing will be performed in alternation. During walking, electrocortical (EEG), hemodynamic (fNIRS), kinematic data (3D motion capture), and kinetic data (force plates) will be recorded. Data will be analyzed to investigate spatiotemporal and frequency characteristics of electrocortical and hemodynamic brain activity related to FOG and the impact of vibrotactile cueing on neural signatures and gait improvements in FOG. Combining neurophysiological measurements of EEG and fNIRS paired with kinematics and kinetics will provide insights into the cortical and behavioral changes associated with FOG and vibrotactile cueing, to derive results that can be used for the design of intervention strategies for treating PD. https://drks.de/search/de/trial/DRKS00034584, identifier DRKS00034584.
The misuse of opioid medications is a significant health issue in the United States. Very few studies have investigated the effect of opioids on perineuronal nets (PNNs), scaffold-like structures that surround neurons and are involved in the regulation of plasticity-dependent mechanisms such as development, learning and memory, and acquisition of addiction-like phenotypes. Regulation of PNNs in the orbitofrontal cortex (OFC) during periods of drug intoxication or withdrawal is widely unknown. In this study, male Wistar rats were injected with fentanyl (0.125 mg/kg, s.c.) or 0.9% saline twice daily for 7 days and once on day 8 (7 continuous days following by 3 days of abstinence) or twice daily for 15 days (5 continuous days followed by 2 days of abstinence for more than 3 weeks) and twice on day 16. Antinociception was evaluated using the tail immersion test immediately before and 30 min after injections. Whole-brain coronal slices were collected, and histochemistry was used to identify Wisteria Floribunda Agglutinin (WFA)-positive PNNs and parvalbumin (PV)-expressing cells. Results confirmed that repeated fentanyl injections induced tolerance to the antinociceptive effects, which normalized following acute abstinence periods. WFA intensity decreased following 8 days of injections. Analyses confirmed significant correlations between PV+ density and tail withdrawal latency following 8 days of fentanyl injections. These data confirm that repeated fentanyl injections modulate both WFA+ and PV+ expression in the rodent brain and antinociceptive tolerance in a duration-dependent manner. Overall, these data suggest that perineuronal nets may mediate opioid-induced behavioral effects, such as antinociceptive tolerance, following repeated administration and abstinence in rats.
Spinal cord injury (SCI) affects 15.4 million people worldwide, with a substantial proportion of incomplete SCI patients remaining non-ambulatory, highlighting the importance of motor function recovery and mobility in rehabilitation. While transcutaneous spinal cord stimulation (tSCS) has emerged as a promising non-invasive neuromodulation technique for enhancing motor recovery, the therapeutic potential of combining tSCS with transcranial magnetic stimulation (TMS) remains largely unexplored. This combination may leverage the complementary mechanisms of supraspinal and spinal neuromodulation to enhance corticospinal tract plasticity and functional motor outcomes. To evaluate the efficacy and safety of combined TMS-tSCS intervention compared to tSCS alone for improving lower extremity motor function in individuals with chronic incomplete spinal cord injury. This prospective, randomized, controlled, assessor-blinded clinical trial will enroll 60 participants with chronic (>12 months post-injury) incomplete spinal cord injury (AIS C or D) aged 18-65 years from Alexandra Hospital, Singapore. Participants will be randomized 1:1 to receive either combined TMS-tSCS (intervention group) or tSCS with sham TMS (control group) for 16 weeks (32 sessions). The primary outcome is change in Lower Extremity Motor Score (LEMS) from baseline to 16 weeks. Secondary outcomes include walking speed (10-Meter Walk Test), functional independence (Spinal Cord Independence Measure-III), spasticity (Modified Ashworth Scale), electromyography of the lower limb muscles and neurophysiological measures of corticospinal excitability. We hypothesize that combined TMS-tSCS will yield superior improvements in LEMS (≥2 points greater improvement) compared to tSCS alone, with enhanced corticospinal tract plasticity as evidenced by neurophysiological measures. ClinicalTrials.gov, identifier: NCT07595497.
Gut-brain axis dysregulation and microbiome-linked metabolic alterations have been implicated in autism spectrum disorder (ASD), but the contribution of gut-derived neuroactive metabolites remains incompletely characterized. We conducted a cross-sectional case-control study of 59 participants (32 ASD, 27 controls) and quantified 18 stool metabolites related to catecholamine synthesis, inhibitory neurotransmission, and tryptophan-linked NAD+-precursor metabolism using targeted liquid chromatography-tandem mass spectrometry. Group differences were assessed using fold-change analysis and linear models adjusted for age and sex. Random forest models evaluated classification performance, and within-group Spearman correlations were used to examine metabolic relationships. Norepinephrine showed the largest increase in ASD, whereas dopamine and tetrahydrobiopterin exhibited nominal group differences that did not remain significant after correction for multiple testing. A three-metabolite panel comprising tetrahydrobiopterin, γ-aminobutyric acid, and kynurenine showed exploratory discrimination between groups (area under the receiver operating characteristic curve = 0.750, 95% confidence interval 0.622-0.878), but this performance requires external validation. Correlation analysis revealed conserved bile acid coupling in both groups. In controls, tryptophan was positively associated with kynurenine, whereas this relationship was not observed in ASD. Instead, ASD samples showed broader associations between tryptophan and metabolites linked to neurotransmission and NAD+-precursor metabolism. Stool metabolite profiling revealed altered organization of tryptophan- and catecholamine-linked metabolic associations in ASD and identified a small metabolite panel with exploratory discriminative potential. These findings provide a foundation for future studies examining gut-derived neuroactive metabolites in ASD and their relationship to gut-brain axis biology.
Neurogenic pulmonary edema (NPE) is a life-threatening complication of acute central nervous system (CNS) injury, characterized by the rapid onset of hypoxemia and pulmonary fluid accumulation in the absence of underlying cardiopulmonary disease. In recent years, emerging integrative frameworks such as the "neuroimmunoaxis" and "brain-lung axis" have provided new perspectives on how CNS injury leads to systemic immune dysregulation and pulmonary dysfunction. However, critical questions remain regarding the interplay between excessive sympathetic activation, immune homeostasis disruption, and lung tissue injury. This narrative review proposes a neurotransmitter-immune-inflammatory model that integrates mechanical, adrenergic, and inflammatory pathways across the spatiotemporal evolution of NPE. We identify four progressive stages involving sympathetic storm initiation due to central autonomic network disinhibition, pulmonary vascular barrier disruption through Piezo channel activation and angiotensin II-norepinephrine synergy, inflammatory amplification from loss of the cholinergic anti-inflammatory reflex, and systemic progression involving gut-lung axis dysregulation. The model generates three testable predictions. Lesions disrupting the nucleus tractus solitarius-ventromedial hypothalamus-intermediolateral column projection should produce more severe NPE. And selective activation of TRPA1+ dorsal root ganglion neurons should attenuate sympathetic outflow and pulmonary edema. Enhancing α7 nicotinic acetylcholine receptor signaling should mitigate systemic inflammation. These predictions offer experimental avenues for validating the hijacking hypothesis. Translational implications include stage-specific interventions, early sympathetic blockade, mid-phase anti-inflammatory and neuro-modulatory strategies, and late-stage lung-protective ventilation. This study aims to offer a comprehensive analysis of NPE by exploring its pathological mechanisms-from central sympathetic signaling to peripheral lung damage. Emphasis is placed on examining the interactions between neural signals, neurotransmitters, and immune responses to uncover the spatiotemporal dynamics of NPE. By identifying potential pathways for early diagnosis and targeted therapies, the research seeks to improve disease management and contribute to better clinical outcomes for affected patients.
Spinocerebellar ataxia type 17 (SCA17) is an autosomal dominant repeat-expansion disorder with marked phenotypic heterogeneity. Cognitive and neuropsychiatric symptoms may dominate early recognition and initially suggest a primary dementia syndrome. We aimed to illustrate diagnostic redirection in dementia-first SCA17 through integrated clinical, imaging, familial, and molecular assessment. We describe the clinical course, neurological findings, cognitive and functional assessments, ancillary investigations, neuroimaging, pedigree information, and molecular genetic findings of a proband with SCA17 and one tested at-risk adult relative. To contextualize the family, we conducted a focused literature review of genetically confirmed SCA17 case and family reports identified through PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang up to April 16, 2026. Repeat-expansion testing established SCA17 in a proband who had initially presented through a dementia-first clinical pathway, with TATA-box binding protein (TBP) alleles of 37/51 repeats. Targeted presymptomatic cascade testing identified the same expanded 51-repeat allele in her asymptomatic adult daughter. Review of 25 published studies showed broad variation in age at onset, TBP repeat size, family context, presenting syndrome, and cumulative phenotype, including cognition-dominant, behavior-dominant, Huntington disease-like, parkinsonian, dystonic, choreic, seizure-associated, and atypical neuroimaging presentations. The present family illustrates a dementia-first route to SCA17 recognition, in which the initial syndrome-based dementia interpretation remained etiologically provisional as cerebellar signs, cerebellar-predominant atrophy, autosomal-dominant family context, and TBP expansion were integrated. This case-based perspective is intended to support etiological reconsideration in selected dementia-first presentations, rather than to serve as validated clinical criteria.
Electroencephalography (EEG)-based emotion recognition provides an objective avenue for affective computing. However, the complexity of EEG signals across temporal, frequency, and spatial domains makes any single dimension inadequate. To overcome these limitations, we propose the Brain Region-Constrained Attention Dual-Branch Network (BRAD-Net). This network adopts a parallel Spatio-Temporal and Spectral-Spatial dual-branch architecture to achieve synergistic multi-domain EEG feature learning. Within the spatio-temporal branch, we introduce a novel Brain Region-Constrained Attention mechanism, which strictly confines self-attention computation to channels belonging to the same brain region. This design not only suppresses irrelevant cross-region interference but also incorporates neuroanatomical priors of brain parcellation, thereby enabling effective and interpretable representation learning. In subject-dependent experiments using 10-fold cross-validation on DEAP and DREAMER datasets, BRAD-Net achieves high accuracies of 97.44%, 97.70%, and 97.97% for valence, arousal, and dominance on DEAP, and 99.66%, 99.78%, and 99.80% on DREAMER, respectively. Leave-one-subject-out validation on DREAMER dataset achieves accuracies of 72.80% and 75.66% for arousal and dominance, respectively. Additionally, the BRAD-Net demonstrates strong cross-paradigm adaptability, achieving 98.21% accuracy on a depression classification dataset. These findings confirm that integrating neuroanatomical priors into a dual-branch multi-dimensional learning framework effectively extracts robust and interpretable neural representations. BRAD-Net not only advances high-performance EEG emotion recognition but also provides a novel, biologically-constrained design paradigm for developing more interpretable brain-computer interface models. By demonstrating that restricting attention to within-brain-region interactions suffices for accurate emotion recognition, our work offers a new theoretical perspective on the application of brain parcellation knowledge in classification models.
Intravenous thrombolysis with alteplase remains the standard reperfusion strategy for acute ischemic stroke (AIS), yet many patients still experience unfavorable outcomes despite timely treatment, underscoring the need for reliable multimodal prognostic markers. To identify independent clinical, laboratory, and neuroimaging predictors of unfavorable 3-month functional outcome in anterior circulation AIS treated with alteplase, and to develop, internally validate, and benchmark an integrated multivariable model in accordance with TRIPOD. This prospective single-center cohort study enrolled 268 consecutive patients with anterior circulation AIS receiving intravenous alteplase within 4.5 h of symptom onset (March 2022-February 2025). Three-month outcome was assessed by the modified Rankin Scale (mRS 0-2 favorable; 3-6 unfavorable) using validated structured instruments administered by blinded raters. Multivariable logistic regression was performed, and the final model was internally validated by 1,000-replicate bootstrap with optimism correction and shrinkage, evaluated by decision curve analysis (DCA), rendered into a nomogram, and benchmarked head-to-head against three previously published reference models using the DeLong test. Of 268 patients, 99 (36.9%) experienced unfavorable outcomes. Seven independent predictors were identified: early neurological deterioration (adjusted OR 3.45, 95% CI 2.01-5.92), large infarction exceeding one-third of the middle cerebral artery territory (OR 2.78, 1.58-4.89), baseline NIHSS (OR 1.14 per point, 1.07-1.22), poor Tan collateral score (OR 2.31, 1.34-3.98), low clot burden score (OR 2.15, 1.28-3.61), elevated D-dimer (OR 2.19, 1.29-3.72), and elevated CRP (OR 1.87, 1.11-3.15). The model achieved an apparent AUC of 0.847 (95% CI 0.801-0.893) and an optimism-corrected AUC of 0.831 (0.785-0.877) on bootstrap validation, with satisfactory calibration (Hosmer-Lemeshow P = 0.394). DCA showed positive net benefit across threshold probabilities of 0.15-0.75, and the model exceeded the recalibrated Hu, Ping, and Lv models. A multimodal panel integrating clinical, inflammatory, coagulation, and neuroimaging parameters independently predicts unfavorable 3-month outcome following intravenous thrombolysis in anterior circulation AIS. The findings are hypothesis-generating pending external validation in independent multicenter cohorts.
Fetal alcohol spectrum disorder (FASD) is associated with neurodevelopmental impairments, including listening difficulties not always explained by peripheral hearing loss, suggesting alterations at the level of neural auditory processing. The frequency-following response (FFR) provides an objective measure of neural speech encoding and may offer insight into auditory function in this population. Twenty-five normal-hearing participants were included: 11 individuals with FASD and 14 controls. Speech-evoked FFRs were recorded using a 160 ms /da/ stimulus at 80 dB SPL with a stimulation rate of 4.35/s. Pitch tracking, stimulus-response correlation, response latency, and signal quality were analyzed using non-parametric statistics. Given the exploratory nature of the study and uncontrolled demographic variables - including a significant age difference between groups - findings should be interpreted as preliminary and hypothesis-generating. Compared to controls, individuals with FASD showed reduced pitch-tracking consistency and lower stimulus-response correlation, with the most robust finding being a large-effect reduction in F0-range correlation (R[70-120 Hz]: p < 0.001, rank-biserial r = 0.823). Prolonged latencies were observed across multiple response components, and signal-to-noise ratio tended to be lower in the FASD group. This exploratory proof-of-concept study provides preliminary evidence of altered neural speech encoding in individuals with FASD despite normal peripheral hearing. Given uncontrolled confounds, observed differences cannot be specifically attributed to FASD. These findings establish the feasibility of FFR in this population and provide the empirical foundation for future controlled research on auditory biomarkers and intervention monitoring in FASD.
Autism Spectrum Disorder (ASD) presents with complex, temporally evolving motor and social behaviours that are difficult to quantify in ecologically valid clinical contexts. While recent computational methods offer diagnostic insights, many depend on fully supervised learning, high-resolution video, or artificial experimental constraints, limiting scalability, interpretability, and privacy compliance. Few approaches leverage unsupervised models to uncover dynamic behavioural structure from minimally invasive inputs. To address these limitations, we propose a privacy-preserving, unsupervised representation learning framework that operates solely on skeletal pose and optical flow features. Using 255 multimodal windows from 15 therapy sessions in the MMASD corpus, a publicly available, privacy-safe dataset of child-clinician interactions, we train a denoising temporal autoencoder to derive compact latent embeddings of behaviour. The model uncovers a low-dimensional behavioural manifold composed of six latent motor clusters. Transition graphs reveal structured topologies, including behavioural hubs and bottlenecks. Saliency analyses identify anatomically and socially relevant features, such as joint pairs (LWrist-LAnkle, Neck-Rear Head) and dynamic flow regions (e.g., index pair 7, 14). Temporal saliency, based on reconstruction error, highlights spontaneous gesture onsets and socially salient events. KL divergence between early and late session phases quantified intra-session adaptation (range: 0.06-17.7) and showed a strong negative correlation with joint attention duration (r = -0.96, p = 0.002), suggesting links between behavioural dynamics and social engagement. These findings offer preliminary evidence that interpretable behavioural structure can be extracted from low-resolution, privacy-compliant inputs. While based on a limited sample, the framework illustrates potential for modeling learning dynamics, identifying salient motor patterns, and supporting objective progress tracking in ASD therapy. Future work will involve clinical validation and application to larger, longitudinal datasets to assess generalizability and therapeutic utility.
Computer vision syndrome (CVS), or digital eye strain, is common among university students exposed to prolonged screen-based near work, yet most preventive approaches rely on passive strategies such as visual breaks, ergonomic advice, or optical filtering. This randomized controlled trial examined whether a 12-week sports vision training (SVT)-enhanced physical education program could reduce CVS symptoms in college students with high daily screen exposure. Two hundred undergraduate students were randomized to an SVT group or a control group, with 100 participants in each group and equal sex distribution. The SVT program integrated BACKNSHOU exercises, a structured sequence of eye, neck, shoulder, and back movements used for visual and postural preparation, Fitlight reaction tasks, SENAPTEC strobe-glasses training involving intermittent visual occlusion during visuomotor activities, multi-target tracking, and sport-based cognitive-motor activities. The control group continued standard physical education and received general 20-20-20 eye-use advice. CVS symptoms were assessed before and after intervention using CVS-SMART, covering visual-fatigue, ocular-surface, and neuromuscular/extraocular domains. At baseline, 149 of 200 participants were classified as CVS-positive, corresponding to a prevalence of 74.5%, with no significant difference across sex-by-group strata. After 12 weeks, CVS-positive prevalence decreased from 76.0 to 44.0% in male SVT participants and from 72.0 to 38.0% in female SVT participants, whereas the control group showed only small, non-significant reductions. Self-reported symptom-specific analyses showed reductions in eye fatigue/soreness, difficulty focusing, red eyes, headache, and neck/shoulder/back pain in the SVT group. Generalized linear mixed models indicated lower post-intervention odds of symptom reporting across all five modeled symptoms, with group effects favoring SVT for all symptoms and reaching statistical significance for eye fatigue/soreness and red eyes. These findings suggest that dynamic, movement-based visual training may complement conventional CVS prevention strategies, although future studies incorporating objective ocular, oculomotor, and neurophysiological measures are needed to clarify mechanisms. This study extends sports vision training from athletic performance contexts to CVS symptom reduction in a non-athlete, screen-exposed student population.
Fetal magnetic resonance imaging (MRI) plays an essential role for the evaluation of fetal abnormalities, offering improved visualization of developing brain structures and superior soft tissue contrast in comparison to other imaging modalities. Accurate and reproducible assessment of fetal brain biometry is critical for diagnosing neurodevelopmental abnormalities. However, these measurements typically rely on manual segmentation, which is time-consuming, labor-intensive, prone to error and dependent on the interpreting radiologist's expertise and experience. Recent advancements have enabled automated analysis primarily on Half-Fourier Acquisition Single-shot Turbo spin-Echo (HASTE) sequences, yet these acquisitions are susceptible to inter-slice misalignment and often require time-consuming super-resolution reconstruction. In contrast, 3D Steady-State Free Precession (SSFP) imaging offers smaller slice thickness, improving through-plane resolution, along with reduced motion sensitivity and whole-body coverage in a single scan. In this study, we present an end-to-end deep learning pipeline for automated segmentation and biometry of the fetal brain from whole-body SSFP MRI. The dataset includes manual annotations of the fetal head, brain parenchyma and extraaxial cerebrospinal fluid (CSF). The framework employs nnU-Net for robust head localization and multi-structure segmentation, along with principal component analysis (PCA)-based reorientation and head circumference estimation. A Tri-Attention U-Net architecture was evaluated as a standalone model and within the nnU-Net framework. The final pipeline consists of a Tri-Attention nnU-Net for head localization and nnU-Net for multi-structure segmentation. The pipeline achieved mean Dice similarity coefficients (DSC) of 94.48, 93.58 and 82.75% for the head, brain parenchyma and extraaxial CSF, respectively. These findings demonstrate the feasibility of accurate, fully automated fetal brain biometry from SSFP MRI with potential to reduce inter-observer variability, streamline clinical workflows and enhance clinical decision-making through fast and reproducible quantitative assessment.
Brain-computer interface (BCI) technology represents a critical frontier in neurorehabilitation. This study aims to systematically analyze the global research landscape, hotspot distribution, and evolving trends of BCI interventions for upper limb rehabilitation in stroke survivors between 2016 and 2025. Bibliometric analysis and systematic mapping were conducted using data from the Web of Science Core Collection and PubMed. Literature was retrieved using terms related to "stroke," "brain-computer interface," and "upper limb rehabilitation." Screening followed the PRISMA guidelines. Visualization and quantitative mapping were performed using CiteSpace (v.6.4.R2) and VOSviewer (v.1.6.20) to evaluate publication volume, international collaboration, and keyword co-occurrence clusters. Annual publications increased steadily from 37 in 2016 to 104 in 2025, with 65.6% published since 2020. The United States (n = 144), China (n = 83), and Italy were the most productive countries. Keyword analysis revealed a paradigm shift from functional electrical stimulation toward robotics-assisted therapy, motor imagery, and AI-driven decoding. Significant burst strengths were observed for "closed-loop systems," "generative AI," and "multi-modal feedback," indicating these as the current primary frontiers. BCI research for post-stroke recovery is transitioning from experimental signal processing to intelligent, multi-modal, and personalized clinical systems. Bibliometric evidence confirms that integrating BCI with robotic-assisted rehabilitation or functional electrical stimulation (FES) has become the mainstream clinical trend. Future efforts must focus on improving EEG signal stability and developing user-friendly hardware to facilitate the transition of BCI from research settings to daily clinical practice. China has emerged as the second most productive country, though international cooperation with European institutions remains an area for further growth.
To evaluate whether local field potential (LFP) spectral profiles can serve as a candidate "spectral fingerprint" for physiological confirmation of centromedian-parafascicular (CM-Pf) targeting during thalamic deep brain stimulation (DBS) for drug resistant epilepsy (DRE). This is a retrospective study of 10 patients (20 leads) who underwent CM-DBS implantation for DRE at a single tertiary center. Postoperative CT and preoperative MRI were co-registered, normalized to the Montreal Neurological Institute (MNI) space, and reconstructed using Lead-DBS software to anatomically localize contacts. BrainSense™ Survey recordings were obtained at least 3 weeks post-implant during routine programming. LFP frequency content was analyzed, and prominent peaks were identified and classified into canonical frequency bands (theta, alpha, beta). These spectral profiles were then mapped to MRI-based anatomical localizations, and statistical tests were applied to assess associations between peak patterns, contact localizations and thalamic subregions. Contacts were distributed as follows: 50 in the CM, 16 in the Parafascicular (Pf), 15 in the Centrolateral, 8 in the Mediodorsal, and 7 in the Ventrolateral (VL) nuclei. Of the 10 representative spectral localizations confined to the CM/CM-Pf region, 8 (80%) displayed a distinct dual-peak spectral profile with peaks in the theta/low alpha (5.5-9 Hz) and high beta (20-30 Hz) bands (mean frequencies: 7.63 Hz and 21.02 Hz, Fisher's exact test, p < 0.001). Single-peak profiles showed no significant association with specific nuclei (p = 0.871). Contacts overlapping other thalamic nuclei more frequently exhibited narrow 10-15 Hz peaks (p = 0.005) or triple-peak profiles (p = 0.02), suggesting mixed structural contributions. A dual- band candidate spectral pattern consisting of theta/low alpha and high beta peaks was associated with the CM-Pf region in this cohort. This finding provides early evidence supporting the feasibility of incorporating passive LFP recordings as a physiologic marker of target engagement. Future work to prospectively compare bipolar survey-based localization with monopolar recording strategies could enable development of a state-based, physiologically informed spectral atlas to refine CM-Pf targeting in thalamic neuromodulation for DRE.
Approximately 40% of patients with chronic kidney disease (CKD) experience cognitive impairment (CI), which is strongly associated with increased mortality. CI is driven by multiple factors, including vascular injury, accumulation of uremic toxins, disruption of the blood-brain barrier, and chronic inflammation. Recent evidence suggests that kidney disease and neurocognitive decline are mechanistically linked through dysregulated tryptophan metabolism. Tryptophan is metabolised through three main pathways: the kynurenine, indole, and serotonin pathways, each producing bioactive metabolites with distinct neurophysiological effects. The hallmarks of CKD include chronic inflammation, gut microbial dysbiosis, and impaired renal clearance, all of which alter tryptophan metabolism. Inflammation drives tryptophan metabolism towards the kynurenine pathway, increasing the formation of neurotoxic compounds that promote oxidative stress, excitotoxicity, and neuronal injury. However, reduced availability of tryptophan for serotonin synthesis impairs serotonergic signalling and neurotransmission, as well as melatonin biosynthesis, thereby contributing to circadian rhythm disturbances and impaired glymphatic clearance. Concurrently, gut dysbiosis and reduced renal clearance promote the accumulation of indole-derived uremic toxins, leading to endothelial dysfunction, neuroinflammation, and disruption of the blood-brain barrier. This review highlights the current evidence of dysregulated tryptophan metabolism in CKD and its impact on the pathogenesis of neurocognitive complications. The review also discusses potential biomarkers and therapeutic strategies, including kynurenine pathway inhibitors, gut microbiota modulation, uremic toxin adsorption, melatonin supplementation and personalised medicine to mitigate cognitive impairment in CKD.
Streaming spiking neural network (SNN) accelerators are widely adopted on edge platforms for their deterministic, low-latency inference. Realizing their full efficiency, however, requires three properties to hold simultaneously: a router-free streaming dataflow, joint exploitation of temporal and spatial sparsity within that dataflow, and configurable parallelism that can adapt to heterogeneous layers and hardware budgets. Existing streaming SNN accelerators typically achieve at most two of these properties: pixel-level and output-channel parallelism are bound to a single fixed operating point, limiting efficient mapping across heterogeneous layers in automatic modulation classification (AMC) workloads. This paper presents a configurable streaming SNN accelerator that satisfies all three properties by explicitly decoupling pixel-level multiple matrix-vector multiplication (MMV) parallelism from output-channel parallelism. The decoupling is realized within a weight-priority gated one-to-all product (GOAP) dataflow by deterministic offline scheduling, preserving router-free streaming execution while exploiting temporal and spatial sparsity. The proposed architecture is implemented on a Xilinx Virtex-7 field-programmable gate array (FPGA) and evaluated using the RadioML 2016 dataset and a compatible subset of RadioML 2018 under multiple sparsity and quantization settings. Experimental results show that, under comparable end-to-end latency, configurable parallelism enables effective layer-wise latency balancing and substantial hardware-resource savings while sustaining high throughput and classification accuracy. More broadly, the same accelerator description can be retargeted across operating points spanning more than an order of magnitude in hardware cost, providing a key enabler for deploying streaming SNNs at scale across diverse edge hardware platforms.
Neural oscillations are not a mechanism that implements cognition. We present a new theoretical framework through a synthesis of relevant literature that has emerged in recent years: metabolic activity in the body, including but not limited to neural tissue, gives rise to an oscillatory pattern that contains information accessible to individual cells. The resulting dynamical structure allows cognitive activity to map the body in fine detail, to perceive its surroundings, or to extend into representations of objects and possibilities never encountered in the world. This array of possibilities is enabled by the coordination of the body's components, which imposes invariant structural regularities among them, in turn creating a moment-to-moment series of states shared across a distributed network of cells. Metabolic success involves ensuring adequate access to nutrition and waste removal for every cell and, when achieved, can give rise to a series of leaps manifested as increased access to complex higher-order affordances. The body's metabolic activity yields observable coordination; however, the mental actions themselves are inscrutable, existing only within a virtual space that unfolds in the interplay of the constituents of a particular body. Access to advanced functions is categorical; this virtual space expands during development and contracts in response to reduced metabolic sufficiency. Markov blankets formalize this asymmetry: brain-scanning technology clarifies the substrate, but no amount of information about the substrate provides direct access to cognitive activity. Frequency bands of oscillation correspond to spatial scales of inter-blanket communication, with cross-frequency coupling carrying information up and down the nested hierarchy. Several clinical conditions-ME/CFS, Long Coronavirus Disease (COVID), cancer-related cognitive impairment, Alzheimer's disease, and age-related decline-share a common upstream mechanism within this framework: cellular damage degrades the substrate, which contracts the space of accessible cognitive operations and produces the categorical incapacity patients report. The framework generates a testable prediction: aperiodic spectral flattening should temporally precede the loss of specific oscillatory peaks as the substrate degrades.
The vagus nerve is the longest cranial nerve and a key component of the autonomic nervous system, functioning as a neurovisceral interface between the brain and peripheral organs. Despite well-defined anatomy, the mechanisms underlying its integrative roles in cardiovascular, metabolic, and neuropsychiatric regulation remain incompletely understood. This narrative review synthesizes current evidence on the anatomical organization, physiological functions, and clinical relevance of the vagus nerve, focusing on cardiac autonomic control, gastrointestinal and metabolic regulation, the gut-brain axis, and vagus nerve stimulation. In the cardiovascular system, it interacts with sympathetic pathways within the cardiac plexus and intrinsic cardiac nervous system to regulate heart rate and conduction. In the gastrointestinal system, it coordinates motility, secretion, and metabolic homeostasis through nutrient- and hormone-sensitive pathways. Within the gut-brain axis, emerging evidence highlights rapid neuroepithelial signaling and microbiota-dependent modulation mediated by vagal circuits. The vagus nerve stimulation represents a promising therapeutic strategy for restoring autonomic balance, although challenges remain in fiber selectivity and clinical variability. Advances in multi-omics approaches are beginning to reveal the molecular heterogeneity of vagal neurons, but significant gaps persist due to limited human anatomical data. In conclusion, the vagus nerve functions as a multidimensional integrative system, and a deeper understanding of its structure and molecular organization is essential for developing precise neuromodulatory therapies.
Detecting brain lesions using Artificial Intelligence methods has been a focus of prior research, with numerous datasets supporting this task to improve clinical results. However, evaluation metrics and reported results often summarise overall performance without considering variations in lesion size. In clinical practice, the detection of small tumours is particularly critical for early diagnosis and treatment effectiveness. This study evaluates the performance of Artificial Intelligence models on established datasets with a specific focus on lesion identification and lesion size. We introduce a novel Deep Learning model tailored to detect small brain tumours in Magnetic Resonance Imaging, integrating a clinically defined "small tumour" concept into both the training and evaluation processes. The proposed approach demonstrates robust performance, achieving loss values ranging from 1.5 to 11.1 and Dice Scores between 96.3% and 98.1% across multiple datasets. The main contribution of this work is the incorporation of clinically meaningful lesion-size information into model development and assessment. These findings suggest that explicitly considering small tumours can improve the clinical relevance of Artificial Intelligence systems for brain lesion detection and support earlier diagnosis and more effective treatment planning.