Integrative migraine management within a healthcare system Part 1: Overview and conventional approaches.

Epilepsy is a complex neurological disorder characterized by nonlinear dynamic interactions among multiple brain regions. The Unscented Kalman Filter (UKF), a high-precision algorithm for nonlinear state and parameter estimation, has recently gained prominence in epilepsy research as it infers latent physiological parameters from electrophysiological signals to reveal the underlying seizure mechanisms. This review provides a comprehensive overview of recent progress in applying UKF to epileptic dynamics modeling and signal analysis, focusing on three major aspects: parameter estimation and model optimization based on neural computational models, seizure detection and prediction, and closed-loop control for seizure intervention. Studies have demonstrated that UKF can robustly reconstruct neuronal dynamics under noise and nonstationary conditions, providing real-time tracking of seizure evolution and contributing to a unified framework that integrates modeling, signal interpretation, and intervention. Despite these advances, important challenges remain, including noise covariance selection, high-dimensional parameter estimation, large-scale network modeling, and limited clinical validation. Future research should focus on adaptive mechanisms, improved multi-parameter estimation, and broader validation using multimodal data and real-patient cohorts. Overall, UKF has shown considerable promise as a model-based framework for epilepsy research and, more broadly, as an interpretable engineering approach for latent neural-state estimation from noisy physiological signals, although broader clinical evidence and further methodological refinement are still required before it can be considered a clinically mature framework.

Multiple sclerosis (MS) is a complex neurological disorder requiring effective patient education. With the increasing use of artificial intelligence (AI) in healthcare, evaluating the readability of AI-generated content compared to evidence-based resources is essential to ensure patient accessibility. This study aims to compare the readability of patient education guides on the diagnosis and management of MS generated by the AI tool Google Gemini with a standard clinical reference, UpToDate. Content was analyzed using the metrics including word count, sentence count, difficult word count/percentage, flesch reading ease (FRE), Flesch-Kincaid Grade Level (FKGL), and simple measure of gobbledygook (SMOG) index. The Wilcoxon signed-rank test was used for statistical comparison. Statistically significant differences (P < 0.05) were found for word count, sentence count, and difficult word count. UpToDate had a significantly higher median word count (6332.5 vs. 800.5) and sentence count (199.5 vs. 44.0) than Google Gemini, indicating it was more verbose and complex. Google Gemini produced content that was relatively easier to read based on FRE, FKGL, and SMOG Index, although these differences were not statistically significant (P > 0.05). Importantly, neither source met the recommended 6th to 8th-grade readability levels for patient health materials. The overall readability scores (FRE, FKGL, and SMOG) were similar, and neither platform delivers information at the comprehension level recommended for the average patient. AI tools such as Google Gemini may serve as a useful adjunct for brief, patient-oriented information, but further refinement is needed to improve accessibility.

Hydrocephalus is a complex neurological disorder traditionally thought to result from excessive cerebrospinal fluid (CSF) production, obstruction of CSF outflow, or impaired absorption. However, these classical theories have limitations in explaining key phenomena observed in certain clinical subtypes, such as normal pressure hydrocephalus. Recent advances in neuro-fluid dynamics have introduced the concept of the glymphatic system, a functional network in the brain that facilitates the exchange of CSF and interstitial fluid (ISF) along perivascular pathways and mediates the clearance of metabolic waste. This new perspective offers a fresh lens for understanding CSF circulation and fluid homeostasis. This review aims to reexamine the pathogenesis of hydrocephalus from the perspective of glymphatic dysfunction and, by integrating anatomical, physiological, and imaging evidence, explore its theoretical implications and clinical relevance.

Depression is a complex mental and neurological disorder and has become one of the most serious public health issues in modern society. Trihexyphenidyl (THY) is a traditional drug used to treat Parkinson’s disease. Recent studies have suggested that it may play a role in regulating neurotransmitters and protecting neurons, but its potential for treating depression has not been fully explored, and how it works remains unclear. Therefore, we examined the effects of THY on depression-like behaviors in zebrafish caused by chronic unpredictable stress (CUS). Our results showed that THY significantly attenuated the CUS-induced decrease in exploratory behavior and shortened the CUS-induced increase in latency time. At the tissue level, THY effectively attenuated the thinning of the optic tectum and the loss of Nissl bodies caused by CUS. In addition, THY reversed the CUS-induced increase in stress hormone levels and reduction in neurotransmitter content. Through network pharmacology and transcriptome sequencing analysis, we found that the mechanisms underlying depression-like behaviors and the antidepressant effects of THY might be related to the MAPK signaling pathway. Further experiments showed that THY regulated the CUS-induced activation of the MAPK signaling pathway, improved the abnormal activation of microglia and damage to astrocytes, and reduced the expression of pro-inflammatory factors, thereby easing neuroinflammation and improving depression-like behaviors. In summary, this study explored the potential mechanism of THY ameliorating depressive-like behaviors and provided basic theoretical evidence.

Artificial intelligence revolutionizing CNS drug discovery and development.

Plant-derived extracellular vesicles as a dual-function nanoplatform for synergistic neurovascular repair in ischemic stroke.

Multidisciplinary review method for novel target identification and prioritization for neurodegenerative diseases.

Developing reliable biomarkers capable of differentiating Parkinson's disease from other neurological conditions is crucial for both patient care and research. In this study, we leveraged recent advances in high-throughput proteomic technology and machine learning to develop candidate biomarkers for Parkinson's disease. Using the Olink Explore 3072 assay, we obtained plasma proteomic profiles from 698 study participants, comprising Parkinson's disease cases (n = 149), neurologically healthy controls (n = 230), and participants with other neurological conditions (n = 319). The study cohort was split into Training Set (n = 560) and Test Set (n = 138). We conducted differential protein abundance analysis and pathway enrichment analysis, and subsequently applied the Boruta algorithm to identify differentially abundant proteins that are predictive of Parkinson's disease. To create a diagnostic biomarker panel, we trained a stacking ensemble machine learning (ML) model on the Training Set (n = 118 Parkinson's patients, n = 184 healthy controls, and n = 258 individuals with other neurological disorders) using eleven proteins (APOH, ARG1, CCN1, CXCL1, CXCL8, DDC, GRAP2, IL1RAP, OSM, PRL, and SPRY2) as model features. We used the Shapley Additive Explanations (SHAP) framework and network analysis to evaluate predictive importance and biological relevance of each protein in the ML model. The model demonstrated high accuracy in the held-out Test Set (n = 138) and three external cohorts-the UK Biobank (n = 43,969), the Parkinson's Disease Biomarkers Program (n = 138), and the Parkinson's Progression Markers Initiative (n = 385), with areas under the receiver operating characteristic curve of 0.939, 0.789, 0.909, 0.816, respectively. Additionally, network and pathway analyses helped interpret the model, revealing activity related to inflammatory mediators, ErbB signaling, T-cell receptor signaling, and lipid metabolism. Our findings highlight the potential of plasma protein biomarkers to improve Parkinson's disease diagnosis and deepen biological understanding of this complex neurological disorder. Our model demonstrates high specificity and reliability across multiple independent cohorts, indicating the significant potential of proteomics-based biomarkers and the clinical utility of ML-supported diagnosis in Parkinson's disease care. The model also helps to elucidate potential novel risk factors and pathways associated with Parkinson's disease.

Although dysregulated serotonergic neurotransmission has been implicated in the pathophysiology of tinnitus, the precise neural circuit mechanisms underlying this complex sensory neurological disorder remain elusive. In the current study, we investigated whether a serotonergic input from the dorsal raphe nucleus (DRN) to the dorsal cochlear nucleus (DCN), a key auditory brain region whose hyperactivity is associated with tinnitus, modulates behaviors in mice consistent with the presence of tinnitus. Using neural tracing and viral-genetic methods, we identified an anatomically and functionally defined serotonergic subpopulation in the DRN that projects to the DCN (5-HTDRN→DCN neurons). Optogenetic activation of 5-HTDRN→DCN circuit increased spike activity in DCN fusiform cells, exhibiting characteristics consistent with tinnitus-like electrical hyperactivity. Chemogenetic activation of 5-HTDRN→DCN circuit induced tinnitus-related behaviors in mice, which was largely reversed by blocking 5-HT2A receptors. Additionally, we found that noise exposure increased 5-HT levels in the DCN and the activity of 5-HTDRN→DCN neurons in mice with noise-induced tinnitus-related behaviors. Importantly, chemogenetic inhibition of 5-HTDRN→DCN circuit ameliorated significantly noise-induced tinnitus-related behavior in mice. These results reveal that activation of 5-HTDRN→DCN circuit induces hyperactivity in the DCN sufficient for the perceptual generation and modulation of tinnitus. These findings provide direct evidence that 5-HT neurons in the DRN play an important role in tinnitus and facilitate our understanding of the circuit mechanisms of pathophysiology in sensory neurological disorders.

Traumatic brain injury (TBI) is a major cause of mortality and long-term disability worldwide, giving rise to complex neurological complications that impact millions of individuals each year. Cellular stress and neuronal injury vary dramatically across cortical layers, vascular niches, and between the ipsilateral (injured) or contralateral (uninjured) hemispheres. There is a critical need for quantitative measures that capture the spatial distribution of injury-induced cellular changes, as well as the gene regulatory elements that drive them. Here, we developed OmicGlaze , an experimental and computational workflow for systematically profiling the spatial transcriptome and epigenome of mouse brains following mild traumatic brain injury. We established a spatial scoring system, and identified region-specific biological processes post injury, including changes in neuronal activities, cellular stress, immune response, and gliosis. Spatial assay for transposase-accessible chromatin with sequencing (Spatial ATAC-seq) generated the first epigenetic map of traumatic brain injury near single-cell resolution. Notably, we identified the Activator Protein-1 family transcription factor Atf3 as a key gene regulator of injury-induced cellular stress. Together, these spatial multi-omics analyses revealed gene regulatory network in TBI and provided a broadly applicable framework for dissecting cellular and molecular mechanisms underlying complex neurological disorders.

Epilepsy remains a complex neurological disorder with a significant proportion of patients exhibiting drug-resistant epilepsy, underscoring the need for novel therapeutic solutions. Despite the availability of numerous antiepileptic drugs, the γ-aminobutyric acid (GABA) system, particularly the GABAA receptor, remains one of the most validated targets for anticonvulsant drug design due to its central role in inhibitory neurotransmission. This review provides a comprehensive analysis of next-generation GABAA receptor modulators, exploring their pharmacological mechanisms, receptor subtype selectivity, and structure-activity relationships. Various heterocyclic scaffolds, including benzodiazepines, imidazoles, thiazoles, oxadiazoles, and bicyclic or fused ring systems, exhibit promising anticonvulsant properties with selective receptor binding and reduced side effects. A systematic literature search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines using PubMed, Scopus, and Web of Science databases from 2018 to 2024. Recent advancements in molecular design, docking studies, and in vivo evaluations highlight how specific molecular modifications enhance receptor affinity, blood-brain barrier permeability, and metabolic stability. Key findings highlight the structural features that enhance receptor affinity, pharmacokinetics, and efficacy. Future research must concentrate on designing these heterocyclic frameworks more efficiently while evaluating their selective receptor binding and performing clinical research to develop advanced anticonvulsant medicines.

Machine learning in epilepsy.

ABSTRACTSchizophrenia is a devastating and complex neurological disorder with poorly understood neurodevelopmental origins. Current rodent models often fail to fully capture human symptomology, hindering therapeutic development. We generated a germline-transmissible DISC1 mutant model in cynomolgus macaques using CRISPR-Cas9 targeting of exon 9. F0 founders and their F1 heterozygous offspring displayed increased stereotypic behaviors and self-injury, reduced exploration, social withdrawal, and sleep fragmentation. Fluoxetine partially ameliorated these behaviors in one founder. Neuroimaging revealed enlarged dorsal striatum (suggesting dopaminergic hyperfunction) and reduced medial amygdala (associated with emotional dysregulation). Plasma metabolomics indicated elevated dopamine and 3-methoxytyramine alongside decreased serotonin metabolite hydroxyindoleacetic acid (HIAA). Notably, one mosaic male exhibited enhanced visual precision and aberrant multisensory integration. Single-nuclei RNA sequencing of the dorsolateral prefrontal cortex revealed an excitatory/inhibitory imbalance—specifically, reduced parvalbumin-positive interneurons and synaptic dysregulation—accompanied by downregulation of autism-risk genes. These macaques recapitulate key psychiatric phenotypes including social deficits, aggression and anxiety, providing a valuable model for screening and testing of targeted therapeutics.

Epilepsy is a complex neurological disorder, ranking as a leading global contributor to disability and death. This study aimed to elucidate the molecular mechanisms underlying microglia-mediated inflammation, apoptosis, and pyroptosis in epilepsy using both cell and animal models. Public datasets (GSE73878 and GSE18740) were analyzed to determine differentially expressed genes. Transcription factors and microRNAs were predicted using UCSC, JASPAR, and TargetScan. BV2 microglial cells underwent lipopolysaccharide stimulation to establish an in vitro inflammation model of epilepsy. Chronic epilepsy was induced in mice using pentylenetetrazole kindling. Flow cytometry, reverse transcription quantitative real-time PCR, ELISA, western blotting, and immunofluorescence staining were employed to delineate the underlying molecular mechanisms. Seizure severity was assessed by electroencephalogram recordings and the Racine scale. In epilepsy models, interferon-induced transmembrane protein 3 (IFITM3) was significantly upregulated and associated with increased levels of cytokines (interleukin [IL]-1β, IL-6, and tumor necrosis factor-α), apoptosis, and pyroptosis-related markers. Signal transducer and activator of transcription 2 (STAT2) directly regulated IFITM3 transcription, whereas let-7g-5p post-transcriptionally suppressed STAT2, leading to indirect downregulation of IFITM3 and thereby mitigating neuroinflammation in epilepsy. The let-7g-5p/STAT2/IFITM3 pathway offers a novel vantage point for formulating new therapeutic modalities against epilepsy.

Over the past decade, organoid research has made transformative advances, emerging as a powerful platform to address key limitations of traditional biomedical models. Although animal systems remain indispensable for studying disease mechanisms, their limited ability to accurately recapitulate human-specific physiology and pathophysiology has contributed to the high failure rate of drug candidates during clinical translation. The emergence of three-dimensional (3D) organoid systems, such as brain and cardiac organoids derived from stem cells, represents a major technological breakthrough. These self-organizing multicellular constructs closely mimic key architectural, cellular, and functional features of native human tissues, enabling more physiologically relevant modeling of complex neurological and cardiovascular disorders. Beyond fundamental biological investigations, brain and cardiac organoids have demonstrated substantial utility in drug screening, toxicity assessment, and precision medicine approaches, including patient-specific disease modeling and therapeutic response prediction. This review highlights recent progress in brain and cardiac organoid technologies, discusses their applications in translational and regenerative medicine, and evaluates their current limitations and future directions in disease modeling and drug discovery.

Alzheimer's disease is a complex neurological disorder and is becoming a global health concern as the population ages. Considering the complex aetiology of the disease and ineptness of single-targeted drugs, the development of multi-targeted drugs emerges as the most effective strategy for the treatment of the disease. Cholinesterases and monoamine oxidases are amongst the most widely explored targets in Alzheimer’s disease, and their dual inhibition offers a promising approach for achieving multipotent therapeutic effects. Herein, we designed and synthesized a series of N/O-propargylated diaryl pyrimidines and evaluated their inhibitory activity against acetylcholinesterase (AChE) and monoamine oxidase (MAO) enzymes. Most of the compounds were found to be active against AChE, MAO-A and MAO-B. Amongst the synthesised derivatives of the series, compounds, compounds NV-1 and NV-9 exhibited a balanced multipotent activity profile against both the targets i.e. acetylcholinesterase and monoamine oxidase. Compounds NV-1 and NV-9 displayed IC50 values of 1.30 µM and 0.88 µM against AChE, 0.232 µM and 9.31 µM against MAO-A and 0.949 µM and 9.23 µM against MAO-B, respectively. In the reversibility inhibition studies, both the compounds were found to be reversible in nature. In kinetic inhibition studies, both NV-1 and NV-9 showed non-competitive inhibition for AChE. Additionally, NV-1 and NV-9 were found to be moderately neuroprotective in nature and exhibit no cytotoxicity at lower compound concentrations. In the partition coefficient studies (octanol/water), the compound NV-9 was found to be lipophilic in nature. Molecular docking studies illustrate their stability within the active cavity of both enzymes. Simulation studies confirmed the thermodynamic stability of these compounds within the cavity for up to 100 ns. Thus, the N/O-propargylated diarylpyrimidines have the potential to be developed as multipotent drugs for the treatment of Alzheimer's disease.

Epilepsy is a complex neurological disorder characterized by recurrent seizures, significantly impacting patient quality of life and healthcare systems worldwide. Conventional EEG analysis for seizure detection is manual and labor-intensive, motivating the integration of advanced artificial intelligence techniques. This study proposes a fully data-driven, scalable deep sequential learning framework that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks for automated seizure detection from EEG signals. Utilizing the publicly available epileptic seizure recognition dataset, the approach preprocesses raw EEG data using segmentation and Hilbert-Huang Transform for time–frequency representation, converting 1D signals into 2D matrices optimized for CNN spatial feature extraction. The hybrid CNN-BiLSTM model leverages convolutional layers to capture spatial traits and BiLSTM to model bidirectional temporal dependencies, facilitating robust classification of seizure versus non-seizure states. Experimental validation demonstrates superior performance compared to CNN, RNN, LSTM, and BiLSTM baselines, with an accuracy of 99.52%, recall of 98.92%, specificity of 99.67%, and minimal false positive and negative rates. The results indicate that the proposed framework achieves near-perfect automated epileptic seizure detection suitable for potential for clinical application. This research underscores the benefits of integrating spatial and temporal deep learning architectures in EEG analysis and provides a promising pathway toward improved monitoring and early intervention in epilepsy management.

South Africa’s extensive floral biodiversity and ethnobotanical history represent a vast, underexplored resource for neurology, with over 300 species traditionally used for CNS ailments like epilepsy, anxiety, and cognitive decline. This paper advocates for a multi-targeted therapeutic strategy as an essential alternative to the insufficient “one drug, one target” conventional approach, given that complex neurological disorders are multifactorial, involving issues like neurotransmitter imbalance, neuroinflammation, and oxidative stress. Plant extracts, rich in bioactive compounds (alkaloids, flavonoids, phenols, and terpenoids), are uniquely suited for this approach, exemplified by Sceletium tortuosum alkaloids acting as serotonin reuptake inhibitors (SRIs) and Boophone disticha alkaloids showing acetylcholinesterase (AChE) inhibitory activity, with other species like Sutherlandia frutescens alleviating mitochondrial dysfunction. However, scientific translation is significantly impeded by a pervasive lack of human clinical trials (RCTs), considerable chemical variability in traditional remedies, and critical ethical and ecological challenges surrounding bioprospecting. To bridge this gap, future efforts must prioritize rigorous clinical validation, implement stringent Standardization and Quality Control (QC) using advanced analytical techniques, and strictly adhere to Access and Benefit-Sharing (ABS) principles to ensure sustainable and equitable commercial development.

Psychosomatic disorders are reported in approximately 10% of clinical populations of children and adolescents. A burden of psychosomatic symptoms is found in about 25% to 40% in both care-seeking and clinical populations. For some of these symptoms the figure is even higher. In contrast, psychosomatic comorbidities may be underdiagnosed in children and adolescents with neurogenetic and complex neurological disorders. We initially investigated this potential underdiagnosis with a systematic literature search. As only few neuropediatric publications on this specific topic were found, publications from neighboring disciplines were included as well. The failure to record comorbid psychosomatic stress could be due to the complex symptomatology of this patient group. Other possible causes concern communication problems within the family and between parents and experts, or insufficient cooperation between the specialist groups involved. Suggestions include potential further development in diagnostic techniques and strategies of communication. New diagnostic and therapeutic perspectives arise when symptoms are understood as functional attempts at adaptation in a developmental psychopathological treatment model. Affected children must also be seen as unique and creative individuals.