Medicine and Pharmacology

Abstract: Delirium remains one of the most pervasive yet least understood forms of acute brain dysfunction in perioperative and intensive care medicine, associated with substantial short-term mortality, prolonged intensive care unit stays, and persistent long-term cognitive impairment in survivors. Despite decades of clinical observation, no single experimental model has bridged the mechanistic gap between molecular neurochemistry, network-level electrophysiology, and the clinical phenomenology of acute brain failure. Animal paradigms incompletely recapitulate human cortical dynamics, whereas bedside neurophysiological monitoring provides only an indirect, downstream representation of the underlying network state. Recent advances in biological computing and organoid intelligence have introduced platforms in which living human-derived neuronal networks, cultured on microelectrode arrays and coupled to computational interfaces, exhibit measurable adaptive electrophysiological behavior and, in some configurations, acquire simple task-related functions through closed-loop feedback. In this perspective, such systems may offer a conceptually informative, although structurally reductive, experimental substrate for mechanistically dissecting delirium, conceived as a paradigmatic failure of adaptive network function. This perspective outlines three perturbational paradigms relevant to anesthesia and intensive care: pharmacological challenge, inflammatory challenge, and a combined-hit model designed to approximate the hypoactive delirium of the septic, critically ill patient. A complementary paradigm is then introduced, based on pharmacologically perturbing acquired network-level functions, and proposed as a substrate for in vitro cognitive pharmacodynamics. Recovery trajectories, network resilience, and the long-term horizon of patient-specific induced pluripotent stem cell-derived cultures for personalized risk stratification are also discussed. The substantial structural, biological, and ethical limitations of current platforms are explicitly acknowledged. These systems lack laminar cortical architecture, thalamo-cortical reciprocal loops, and ascending neuromodulatory systems central to anesthetic pharmacology and to several leading neuropathogenetic theories of delirium. Nonetheless, biological computing systems may eventually serve as a translationally meaningful intermediate layer between molecular neuroscience and whole-brain clinical physiology, with delirium serving as a paradigmatic and clinically urgent test case.

Abstract: Background: Triptans and lasmiditan are considered specific acute medications for migraine attacks. Despite the efficacy of these treatments, continuation of these medications was limited because some patients experienced adverse effects. A previous study reported a close association between migraine and neurodevelopmental disorders. The present study investigated triptans and lasmiditan in the treatment of consecutive patients with migraine attacks accompanied by neurodevelopmental disorders. Methods: The enrolled patients were diagnosed with migraine by a certified headache specialist. Brain magnetic resonance imaging was performed in all patients, and all other organic lesions causing headache were excluded. Neurodevelopmental disorders were diagnosed by multiple specialists. Triptan and/or lasmiditan tablets were prescribed for all patients. Results: Triptans were effective in these patients, providing pain relief within 2 h in all patients, and eight patients (73%) were pain-free for 2 h. However, throat discomfort was detected in two patients, and it was regarded as a minor adverse event. Lasmiditan (50 mg) was prescribed to 10 patients, and it provided pain relief for 2 h in 9 patients (90%) and pain-free for 2 h in 5 patients (50%). Conversely, mild somnolence was detected in two patients. Conclusions: In this study, triptans and lasmiditan were effective and safe in the treatment of migraine attacks accompanied by neurodevelopmental disorders.

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition commonly assessed through clinical interviews, behavioral observation, and rating scales. Although electroencephalography (EEG) has emerged as a promising complementary tool for ADHD assessment, robust subject-independent classification remains challenging due to inter-subject variability, limited datasets, and the need for interpretable computational models. This work introduces EEG-TACT, a compact end-to-end deep learning architecture for identifying ADHD subjects from EEG epochs. The proposed model integrates an EEGNet-inspired convolutional embedding, a Transformer encoder operator, and an attention-based pooling mechanism to jointly capture local spatiotemporal EEG patterns, contextual temporal dependencies, and task-relevant latent representations. EEG-TACT was evaluated on a publicly available EEG dataset using strict, subject-independent stratified group partitions, ensuring that data from the same subject were never shared across the training, validation, and test subsets. Model interpretability is examined through learned temporal filter responses, class-conditioned self-attention maps, and latent-space projections, while an ablation study quantifies the contribution of each architectural component. Performance was assessed at the fold, subject, and epoch levels, together with statistical significance comparisons against representative state-of-the-art architectures. EEG-TACT outperformed the contrasted models, achieving subject-level accuracy of 87.5%, recall of 96.0%, and precision of 82.8%, while requiring only a few thousand trainable parameters. By exhaustively repeating the initialization, the proposed model demonstrated improved labeling reliability and achieved the best average ranking among the evaluated architectures. The reported results therefore prove that EEG-TACT provides a compact, robust, and interpretable model for EEG-based ADHD identification under subject-independent evaluation settings.

Abstract: Background: Invasive neuromodulation therapies such as deep brain stimulation (DBS) have become increasingly important for treating refractory neurological and psychiatric disorders, including movement disorders, epilepsy, obsessive-compulsive disorder, and more. Despite increasing clinical use, optimizing brain stimulation remains a major challenge. Key questions remain regarding how to select optimal stimulation targets, determine electrode placement, program the large parameter space of stimulation settings, understand mechanisms of action, and adapt therapy as symptoms fluctuate or progress over time. Because direct clinical symptom measurement can be variable, delayed, or difficult to quantify intraoperatively, physiologic biomarkers hold potential to navigate these challenges. One promising approach is to measure the neural responses elicited by brief pulses of electrical stimulation, based on the assumption that stimulation-evoked potentials reflect the complex connectivity of activated brain networks. Objective: Following PRISMA guidelines, we systematically review studies investigating how such stimulation-evoked potentials, particularly cerebro-cerebral evoked potentials (CCEPs), can improve invasive brain stimulation in humans. Results: Across movement disorders, epilepsy, and psychiatry, stimulation-evoked potentials have been used to determine network engagement, refine target localization, guide postoperative device programming and, in emerging cases, inform adaptive or state-dependent stimulation strategies. Conclusion: We highlight common methodological approaches, key findings, and limitations across these applications. Finally, we discuss future directions needed to transition from retrospective circuit characterization toward prospective, patient-specific guidance of brain stimulation therapies.

Abstract: Background: Knee osteoarthritis (KOA) is a highly prevalent degenerative joint disease with a growing global disease burden, yet traditional interventions have limited efficacy. Neuromodulation has emerged as a promising non-pharmacological rehabilitation strategy for KOA, but existing reviews have not systematically synthesized the clinical protocols, efficacy characteristics, and full spectrum of research gaps for mainstream neuromodulation techniques. This scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, aiming to describe the current application status and identify critical research gaps. Methods: A systematic search was performed in PubMed and Web of Science databases for clinical studies on neuromodulation for KOA published between May 13, 2021 and May 13, 2026. Two researchers independently completed literature screening, data extraction, and cross-verification according to pre-specified inclusion and exclusion criteria. Results: A total of 131 records were initially retrieved. After deduplication, title/abstract screening, and full-text evaluation, 23 studies were finally included, comprising 18 randomized controlled trials, 3 self-controlled before-after trials, 1 retrospective case series, and 1 cross-sectional study. The included studies covered three core categories of neuromodulation: peripheral neuromodulation (TENS, NMES, PNS, electroacupuncture, electrical dry needling, tVNS), central neuromodulation (tDCS), and combined neuromodulation (TENS + tDCS). All techniques demonstrated certain efficacy in pain relief and functional improvement, but intervention protocols varied widely, and optimal parameters have not been standardized. Conclusion: Neuromodulation, as a non-pharmacological rehabilitation strategy, has broad application prospects in KOA treatment. However, existing studies still have deficiencies in intervention standardization, long-term efficacy evaluation, and mechanism exploration. Future high-quality, large-sample studies are needed to optimize clinical protocols and clarify mechanisms of action, providing evidence support for the standardized application of neuromodulation in KOA rehabilitation.

Abstract: Introduction: Decompressive craniectomy (DC) is often required to stabilize the intracranial pressure (ICP) in patients with traumatic brain injury (TBI). Both the sinking flap syndrome and hydrocephalus are known complications of DC. The pathophysiology of each is unknown. Case Report: We report on a patient who underwent DC for TBI who suffered both the sinking flap syndrome and low-pressure hydrocephalus. We measured the changes in volumes of each hemisphere and the ventricles with CT, and the cerebral blood flow (CBF) and aqueduct flow with phase-contrast MRI during different stages of the disease process. Discussion: The sinking flap syndrome in this patient was associated with a reduction in volume of both supratentorial cavities. There was a significant reduction in CBF bilaterally, which increased by an average of 26% following cranioplasty. During the low-pressure hydrocephalus phase of the patient’s illness, there was a reversed CSF flow directed toward the ventricles. Once the ventricles returned to normal size, this reversed flow was lost. Lumped parameter modelling of the patients’ CSF and vascular systems suggests that the reduction in blood flow was due to a reversible constriction of the arterioles secondary to a reset of the autoregulation rather than compression of the venous structures. There is evidence to suggest there is an increase in CSF absorption efficiency despite the known CSF absorption mechanisms being unlikely to function at such a low ICP. A hypothesis is put forward that CSF absorption occurs via the brain capillary bed in these diseases.

Abstract: Background and purpose Persistent Post-Concussive Symptoms (PPCS) develop in 15-20% of individuals following mild traumatic brain injury (mTBI), representing a global health burden. This open-label cohort study investigated the effects of individualized, algorithm-based intermittent hypoxic-hyperoxic conditioning (IHHC) on pain and health-related quality of life (QoL) in PPCS patients. Method A total of 158 consecutive patients (70% female; mean age 40.8 years; mean symptom duration 28.5 ± 21.4 months) received IHHC protocols tailored to symptom duration, baseline pain, QoL, and demographics, and was followed up 6-weeks post-treatment initiation. The protocols consisted of a median of 7 sessions (mean 9.1, range 3-35), each comprising intermittent cycles of 3-8 minutes of hypoxia (FiO2 8.5-13%) and 1-3 minutes of hyperoxia (FiO2 34-36%). 44 of the 158 patients completed the 6-month questionnaire, constituting a 6-month follow-up cohort. Pain intensity (NRS) and QoL (SF-36) were assessed at baseline, 6 weeks, and 6 months. Results Baseline SF-36 scores were 38.9-40.5% below the Danish population norms. SF-36 scores (8 domains) increased by 23.6% (p< 0.0001) at 6 weeks and 33.4% (p< 0.0001) at 6 months. Physical (PCS) and Mental (MCS) Component Summary scores improved sig-nificantly in both cohorts; the largest gain was MCS at 6 months (+8.06 points; p< 0.0001). At baseline, 69.0% and 77.2% of patients reported pain with an intensity NRS-score ≥ 3 during rest or activity, respectively, and headache was the predominant pain source (81.7% of patients with NRS ≥ 3). Pain intensity decreased by 24.0% and 21.0% (p< 0.001) at 6 weeks, and by 44.4% and 41.3% (p< 0.001) at 6 months during rest and activity, re-spectively. Conclusion Individualized IHHC was associated with reduced headache-associated pain and improved QoL in PPCS patients. Controlled trials are warranted to confirm these findings.

Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) are increasingly understood as systemic disorders driven by chronic neuroinflammation, metabolic dysregulation, and barrier dysfunction, which interact dynamically along the gut–immune–brain axis. The Mediterranean diet rich in plant-based foods, olive oil, and fish, is consistently associated with reduced cognitive decline and neurodegeneration risk. This review synthesizes recent advances to present a comprehensive framework illustrating how the Mediterranean diet functions as a systems-level modulator. Mechanistically, the Mediterranean diet remodels the gut microbiota, enhancing the production of bioactive metabolites like SCFA-producing bacteria (SCFAs). These metabolites serve as key signaling mediators that reinforce intestinal barrier integrity, reduce systemic inflammation, and subsequently modulate central processes. Within the central nervous system, diet-derived cues influence neuroinflammation by reprogramming microglial and astrocytic states, support mitochondrial function and proteostasis, and help maintain blood–brain barrier (BBB) stability. Disease-specific insights for AD and PD highlight the diet's role in modulating hallmark pathologies such as Amyloid Beta (Aβ), tau, and α-synuclein aggregation. Emerging multi-omics technologies—including single-cell/spatial transcriptomics and microbiome profiling—are reshaping the field, offering unprecedented resolution to dissect these pleiotropic effects. Ultimately, while the Mediterranean diet presents a promising neuroprotective strategy, individual responses vary based on genetics, microbiome, and metabolic context. The integration of these technologies is pivotal for transitioning from generalized dietary advice to precision nutrition approaches tailored to individual patient profiles, positioning the Mediterranean diet not merely as a diet but as a programmable intervention for neuro-immune and metabolic network modulation. Key challenges remain, including the need for more randomized controlled trials (RCTs) and standardized frameworks for multi-omics integration.

Abstract: Electroencephalography (EEG) provides a non-invasive alternative for supporting Attention-Deficit/Hyperactivity Disorder (ADHD) assessment, but existing classification pipelines often depend on handcrafted descriptors, segment-wise decisions, or deep neural architectures whose interpretability and subject-level generalization remain limited. This work introduces Hidden Markov Model-Induced Stationary RKHS Distance Learning (HIS), a probabilistic-kernel framework for interpretable EEG-based support for the diagnosis of ADHD. In the proposed approach, each subject is represented by a Hidden Markov Model with Gaussian-mixture emissions, trained on frontal EEG recordings. Rather than vectorizing the learned parameters, each HMM is mapped to its induced stationary observation distribution, which is then embedded into a Reproducing Kernel Hilbert Space. Pairwise subject dissimilarities are computed through a closed-form Hilbert embedding distance between stationary Gaussian mixture distributions and used by precomputed-kernel classifiers. The method was evaluated on a controlled synthetic EEG benchmark and on a public pediatric ADHD EEG dataset recorded during a visual attention task. The proposed HIS distance was compared against the Probability Product Kernel, a finite-horizon HMM similarity baseline. On synthetic EEG, HIS achieved 95.0% held-out test accuracy and consistently outperformed the baseline across classifiers. On the real EEG dataset, the best configuration used a compact HMM topology and KNN classification, reaching 95.8% held-out test accuracy at the subject level. Qualitative t-SNE analyses further showed that HIS induces more discriminative local subject neighborhoods than the baseline kernel, while avoiding segment-level sample inflation. These results suggest that stationary RKHS embeddings of subject-specific HMMs provide a competitive, leakage-aware, and interpretable framework for modeling variable-length EEG recordings in ADHD classification.

Abstract: Background/Objectives: This study aimed to examine the associations between diet-related quality of life (DRQOL) and psychological distress, autonomic dysfunction, and migraine in patients with dizziness and balance disorders. Methods: In this retrospective cross-sectional study, 122 patients (56 men, 66 women; mean age 40.4 ± 12.8 years, minimum 14, maximum 65) from the vertigo outpatient clinic at JR Tokyo General Hospital completed self-reported questionnaires. These included the DRQOL scale, Dizziness Handicap Inventory (DHI), Hospital Anxiety and Depression Scale (HADS), Self-rating Depression Scale (SDS), Orthostatic Dysregulation (OD) checklist, and migraine assessments (POUNDing [Pulsating, duration of 4–72 h, Unilateral, Nausea, Disabling], MIDAS, migraine screener). Correlational analyses, group comparisons, and receiver operating characteristic (ROC) analyses were conducted. Results: DRQOL scores showed positive correlations with psychological distress (SDS: ρ = 0.58; HADS-A: ρ = 0.50; HADS-D: ρ = 0.52; all p <  0.001) and OD severity (ρ = 0.48, p <  0.001), but not with age, DHI, or individual migraine indices. Migraine screener-positive patients had significantly higher DRQOL scores (p <  0.01). DRQOL alone modestly discriminated positive migraine screener (AUC = 0.65), improving to AUC = 0.77 in a multivariable model including age and sex. Conclusions: DRQOL can capture psychological and autonomic symptom burden rather than vestibular or headache severity, suggesting that it may serve as a complementary, patient-centered metric in the holistic assessment of dizziness patients.

Abstract: Chronic migraine affects 1–2% of the global population and is the leading cause of neurological disability among women under 50 years of age. The advent of calcitonin gene–related peptide (CGRP)-targeting monoclonal antibodies and small-molecule receptor antagonists has constituted the first disease-specific preventive paradigm; nonetheless, real-world registries demonstrate that 30–50% of treated patients fail to revert to an episodic phenotype, with medication-overuse headache further complicating clinical management. The therapeutic ceiling observed with single-target CGRP pharmacology implies that chronification is governed by mechanisms operating upstream of, in parallel with, and beyond the trigeminovascular neuropeptide loop. The present narrative review synthesises converging evidence from 2020 to 2026 and advances a multi-stratum model in which chronic migraine is conceptualised as an emergent systems failure. Within the trigeminocervical complex, the alarmin high-mobility group box 1 (HMGB1) is proposed to function as an upstream catalyst of the Toll-like receptor 4 (TLR4)–NF-κB–CGRP signalling axis; murine nitroglycerin models indicate that HMGB1 silencing attenuates neuroinflammation and central sensitisation. Clinical data obtained from patients with medication-overuse headache reveal elevated circulating concentrations of lipopolysaccharide, HMGB1, and hypoxia-inducible factor 1-alpha, consistent with intestinal-barrier compromise driving sustained systemic neuroinflammation. Preclinical findings from 2026 document sex-specific perturbations of the gut microbiota and faecal metabolome, together with augmented allodynia in female chronic-migraine models; complementary work demonstrates that sleep restriction and caffeine synergistically reduce the trigeminovascular activation threshold in a sex-dependent manner. Functional neuroimaging implicates sustained decoupling of the salience, default-mode, and central-executive networks as the putative neural substrate of interictal cognitive morbidity. A complementary computational account, grounded in the Free Energy Principle, conceptualises chronification as the consolidation of pathologically rigid prior beliefs—a hypothesis amenable to falsification via task-based contingent-negative-variation, mismatch-negativity, and Hierarchical Gaussian Filter modelling of probabilistic-learning paradigms. It is concluded that progress in chronic-migraine research requires a transition from single-target optimisation toward multi-stratum intervention, anchored in a longitudinal transitional cohort with integrated neuroimaging, electrophysiological, microbial, and ecological-momentary endpoints.

Abstract: Background: Neuroinflammation contributes to etiopathology and symptom severity in neurodegenerative and neuropsychiatric disorders. Glial cells, especially microglia and astrocytes, play a crucial role in neuroinflammation. It has been reported that ginseng and its bioactive component ginsenoside Rg1 exhibit anti-inflammatory effects and improve cognitive performance in various models. However, the exact underlying mechanisms remain unclear. Methods: An astrocyte-microglia co-culture model simulating physiological (M5, 5-10% microglia) and pathological/inflammatory (M30, 30-40% microglia) conditions was treated with different concentrations of ginsenoside Rg1 (15, 30, 45 µM), ginseng extract (derived from Korean red ginseng) at low-dose (12.5, 25, 37.5 µg/ml) or high-dose (125, 250, 375 µg/ml) for 24 hours. Cell viability was assessed by MTT assay. Microglial reactivity was examined by immunocytochemistry. Astrocytic gap-junctional coupling was investigated using scrape loading method and connexin 43 (Cx43) expression was analyzed by immunocytochemistry and Western blot. Results: Both Rg1 and low-dose ginseng extract reduced microglial activation under inflammatory conditions by promoting a phenotypic shift from activated to homeostatic (resting) microglia. Rg1 preserved astrocytic gap-junctional function by preventing the inflammation-induced downregulation of Cx43 expression and enhancing Cx43-mediated gap-junctional intercellular communication. Rg1 caused a significant reduction of glial cell viability only at high concentrations (30 and 45 µM) under inflammatory conditions. High-dose ginseng extract showed significant concentration-dependent cytotoxicity, reducing glial cell viability under physiological and pathological conditions, without comparable anti-inflammatory benefits. Conclusions: This study suggests that low-dose ginseng and its active compound Rg1 exert anti-inflammatory effects by modulation of astrocytic coupling and microglial reactivity. These results provide a novel therapeutic perspective for ginseng in the treatment of neurodegenerative and neuropsychiatric diseases related to neuroinflammation.

Abstract: Anxiety symptomatology and excess weight are associated with chronic low-grade inflammation. Olive Leaf Extract (OLE) contains polyphenols with antioxidant and anti-inflammatory properties that have shown anxiolytic like effects in experimental models; however, evidence in humans remains limited. This randomized double-blind placebo-controlled pilot trial evaluated the effects of OLE supplementation on anxiety symptomatology, inflammatory markers, and metabolic parameters in women with excess weight and mild to moderate anxiety symptoms. Participants received OLE (750 mg/day) or placebo for 12 weeks. Anxiety symptomatology was assessed using HAM-A, BAI, and STAI, while inflammatory and metabolic parameters were evaluated at baseline and post intervention. OLE supplementation was associated with a significant reduction in HAM-A scores, particularly psychic anxiety symptoms, together with lower TNF-α levels compared with placebo at the end of the intervention. No significant differences were observed in body composition, caloric intake, IL-6, hs-CRP, cortisol, or most metabolic parameters. Correlation analyses revealed positive associations between inflammatory markers, fat mass, and anxiety related measures. These findings provide preliminary evidence suggesting that OLE supplementation may exert beneficial effects on psychic anxiety symptomatology and inflammatory activity in women with excess weight. However, larger randomized clinical trials are necessary to confirm these observations and clarify the underlying mechanisms.

Abstract: Chronic migraine is increasingly understood as a network-level disorder in which trigeminovascular nociception is sustained by metabolic, inflammatory, and macro-network dysfunction rather than by an isolated headache mechanism. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), originally developed for type 2 diabetes and obesity, engage receptors expressed in choroid plexus, cortex, hippocampus, thalamus, and hypothalamus. Evidence converges on four mechanistic intersections between GLP-1 signalling and the chronic-migraine cascade: restoration of cerebral insulin signalling and cortical energy supply; modulation of cerebrospinal-fluid secretion and intracranial pressure with downstream relevance for glymphatic dynamics (most clearly validated in idiopathic intracranial hypertension, with explicit caveats for normotensive migraine); suppression of microglial activation in pain-relevant circuits (established preclinically, with one pilot human imaging study); and direct trigeminal-nociceptor effects via TRPV1 inhibition demonstrated for exendin-derived peptides. A 2026 real-world cohort of approximately 11,000 chronic-migraine adults provides a preliminary clinical signal. We propose, and operationalise as falsifiable, the hypothesis that GLP-1RAs are disease-modifying rather than purely symptomatic in chronic migraine, and we describe a placebo-controlled trial with parallel multiple mediation analysis on weight change AND HOMA-IR/TyG delta that can refute the claim within five years. A biochemical scheiding between exendin-based agonists (exenatide, lixisenatide) and human GLP-1 analogues (semaglutide, liraglutide, dulaglutide) generates a falsifiable sub-hypothesis on peripheral TRPV1 inhibition that is testable retrospectively on data that already exist.

Abstract: Ultraweak photon emission (UPE) refers to spontaneous, low-intensity photon release from biological systems, generated largely through oxidative metabolic reactions involving reactive oxygen species, lipid peroxidation, mitochondrial activity, and electronically excited molecular intermediates. Because the nervous system is highly metabolically active and vulnerable to oxidative stress, hypoxia, excitotoxicity, inflammation, and mitochondrial dysfunction, UPE may offer a noninvasive optical window into neural physiology and disease. In this narrative review, we examine experimental and translational evidence linking UPE to nervous system function, with emphasis on neuronal excitation, glutamate-mediated activity, ischemia-reperfusion injury, stroke, neurodegeneration, mental-state and anesthesia paradigms, photobiomodulation, demyelinating disease, Parkinson disease, amyotrophic lateral sclerosis, and neuro-oncology. Across these domains, UPE appears most consistently associated with redox metabolism, mitochondrial function, oxidative stress, and excitation–metabolism coupling, whereas evidence that endogenous photons mediate functional neural signaling remains preliminary. Current data suggest that UPE may be most promising as a preclinical biomarker of tissue metabolic state, delayed post-ischemic dysfunction, and early neurodegenerative change, particularly when integrated with electrophysiology, perfusion imaging, molecular assays, and other physiologic measures. However, clinical translation is limited by low photon flux, limited temporal and spectral resolution, difficulty localizing signals from deep tissue, heterogeneous experimental protocols, and incomplete source attribution. Overall, UPE represents a promising but still early-stage framework for studying nervous system metabolism and disease, with future progress dependent on standardized methods, multimodal validation, and disease-specific investigation.

Abstract: Persistent post-concussion symptoms (PPCS) following mild traumatic brain injury (mTBI) are common and frequently disabling. However, symptom persistence is often poorly correlated with injury severity or structural brain abnormalities. Increasing clinical and research evidence suggests substantial overlap between PPCS and functional neurological disorder (FND), yet this interface remains poorly synthesised and conceptually unresolved. To systematically review and synthesise the evidence linking mTBI with functional neurological symptoms, and to refine existing conceptual models by proposing a clinically useful framework for differentiating functional and organic contributions to persistent post-concussion presentations. A scoping review with narrative synthesis were conducted. Database searches yielded 120 records; after duplicate removal and abstract screening, 32 studies underwent full-text review. Included studies comprised systematic reviews, narrative and conceptual reviews, mechanistic hypothesis papers, primary observational studies, case series, case reports, and early interventional and neu-roimaging investigations examining functional neurological symptoms in the context of mTBI. The literature demonstrates substantial phenomenological overlap between PPCS and FND across cognitive, motor, sensory, visual, and seizure-related domains. Functional neurological symptoms can emerge after concussion and may closely resemble PPCS, often in association with psychiatric comorbidity, dissociation, trauma exposure, and maladaptive attentional or illness-belief processes. Objective neurological impairment and injury severity show weak and inconsistent associations with symptom persistence. The evidence base is dominated by clinic-derived observational studies, with no population-level incidence estimates identified. Functional neurological symptoms represent a significant and under-recognised contributor to persistent symptoms after mTBI. Existing evidence supports moving beyond binary organic–psychogenic models toward a functional–organic differentiation framework that acknowledges dynamic interactions between injury-related and functional mechanisms. Improved screening, diagnostic communication, and stratified management are likely to enhance outcomes for patients with persistent post-concussion symptoms.

Abstract: Background: As the society ages, the number of patients with early cognitive impairment that can progress to Alzheimer’s disease also increases. Early diagnosis and risk as-sessment allows effectively initiate the necessary lifestyle changes and monitoring. The use of artificial intelligence (AI), when analyzing medical histories, enables more pro-ductive evaluation of large datasets and identify patterns that may go unnoticed in clinical practice. This kind of approach can improve early screening, reduce physicians’ workload and develop bigger support for personalized treatment. The aim of the study: To compare the performance of machine learning (ML) algorithm with a physician (neurologist) in assessing patient’s subjective cognitive decline and Alz-heimer’s disease risk in early stages. Research methods: The research was designed as a retrospective, comparative cohort study that used two data sources. Firstly, the National Alzheimer’s Coordination Center (NACC) longitudinal dataset to train the ML model. Secondly, medical records gathered from Pauls Stradins Clinical University Hospital dating from 2020 till May 2025 to evaluate the al-gorithm’s precision. Results: The research included 154 patients, predominantly women (68.8%), with a mean age of 80.3 years. Class distribution consisted of dementia (n=139); mild cognitive im-pairment (MCI) (n=13); subjective cognitive decline (SCD) (n=2). Dementia was identified the best – 128/139 (accuracy – 92.1%) with errors tending towards MCI. MCI was correct in 9/13 cases (accuracy – 69.2%) All SCD cases were classified as dementia. Overall model’s accuracy was 91.6% (141/154). Conclusions: ML algorithm can match to neurologist made diagnoses with high precision but is struggles to separate adjacent early-stage diagnoses. At this moment, ML models are great decision supporters, but no yet alone diagnosticians. Nevertheless, this technology has high potential to being integrated in the future to aid triage and early screening, especially when advanced diagnostics are limited.

Abstract: Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology.

Abstract: Objective: To synthesise the pathobiology of cortical spreading depolarization (CSD) and critically appraise current and emerging pharmacological strategies specifically targeting migraine aura prevention. Background: Migraine with aura affects 25–30% of patients, and the aura phenomenon remains a substantial unmet preventive need. Calcitonin gene-related peptide (CGRP) monoclonal antibodies do not readily cross the blood–brain barrier and frequently fail to suppress CSD, the neurophysiological substrate of aura.Methods: A literature search of PubMed, Embase, and the Cochrane Library (inception through January 2026) identified studies on CSD pathophysiology, preclinical CSD suppression, and clinical efficacy of candidate agents. Evidence quality was assessed with GRADE; risk of bias with Cochrane RoB 2 (RCTs) and ROBINS-I (observational); narrative synthesis followed SWiM. De novo quantitative estimations (post-hoc power analyses, sample-size projections, worst-case sensitivity analyses) were used as methodological tools, not as original empirical data. Results: CSD pathogenesis is organised into four phases: pre-CSD vulnerability, initiation, glial propagation, and neuro-inflammatory transduction. Lamotrigine and memantine target initiation and have the most advanced clinical evidence; both lack aura-specific RCTs. A 2024 network meta-analysis ranked memantine favourably (50% responder rate OR 5.58, 95% CI 2.41–12.92) but no contributing trial stratified by aura. An a priori sample-size calculation indicates 214 enrolled patients (170 evaluable; NNT≈4.9; n/(1−d) for 20% attrition) for a definitive aura-specific memantine RCT. Tonabersat—a Cx36/Cx43 gap-junction modulator—reduced aura attacks from 3.2 to 1.0 per 12 weeks in Phase 2; a worst-case intention-to-treat sensitivity analysis confirms that this signal survives even 16.6% unaccounted attrition. Spironolactone (pannexin-1 inhibition) and amiloride (ASIC1a) remain preclinical or pilot-stage. Tissue-selective KATP antagonists (Kir6.1/SUR2B) and the anti-PACAP-38 antibody Lu AG09222 represent the most promising pipeline agents. Conclusion: The therapeutic gap for migraine aura prevention reflects correctable methodological choices, not a lack of biological tractability. Four mechanism-based drug classes—NMDA-receptor antagonists, pannexin-1 inhibitors, gap-junction modulators, and KATP antagonists—offer entry points for aura-specific prevention. Adequately powered, aura-enriched RCTs with validated CSD biomarkers (DC-EEG co-registered against electrocorticography; neuron-derived extracellular vesicles) and pre-specified falsifiability thresholds are now the rate-limiting step. Seven testable methodological predictions are proposed.

Abstract: Background: Plasma biomarkers are widely promoted as scalable tools for staging Alzheimer’s disease (AD), yet head-to-head comparisons against the clinical scales used to define diagnostic labels remain scarce. Reported gains from machine-learning fusion of clinical and biomarker features may therefore reflect label circularity rather than biological signal. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we assembled 655 participants (CN = 296, MCI = 168, AD = 191) with concurrent plasma biomarkers (pT217, Aβ42/40, NfL, GFAP), clinical scales (MMSE, CDR-SB, FAQ), APOE genotype, and demographics. Three pre-specified feature sets (clinical-only, biomarker-plus-demographic-genetic, and full fusion) were compared across four classifiers (Logistic Regression, SVM, Random Forest, XGBoost) using repeated nested cross-validation (5-fold × 3 outer, 5-fold inner) with balanced class weighting. External validation used the Center for Neurodegeneration and Translational Neuroscience (CNTN) cohort (n=130). Results: Clinical scales alone reached a three-class AUC-OvR of 0.9539±0.0041, and fusion reached 0.9559±0.0046, an indistinguishable gain. Because MMSE, CDR-SB, and FAQ partly determine ADNI diagnostic labels, both estimates are circularity-inflated upper bounds. Independently of this circularity, the plasma-plus-demographic-genetic model still achieved AUC-OvR =0.7455±0.0150, with pT217 the dominant contributor. Pairwise discrimination was excellent for CN vs. AD (1.0000) and MCI vs. AD (0.9739), but markedly weaker for CN vs. MCI (0.9302 fused, 0.69 plasma-only). The reduced biomarker model transferred to CNTN with AUC-OvR =0.702 (95% CI 0.635–0.764). Conclusions: Apparent fusion gains in ADNI are largely a consequence of label circularity. After removing the circular clinical features, plasma pT217 supports three-class CN/MCI/AD screening at AUC ≈0.74 internally and 0.70 externally, which establishes a realistic performance ceiling for blood-based AD staging. MCI detection remains the principal bottleneck.