Complex Neurological Disorder Research Articles

Sarcopenic obesity and brain health: A critical appraisal of the current evidence.

Sarcopenic obesity (SO) is a body composition phenotype derived from the simultaneous presence in the same individual of an increase in fat mass and a decrease in skeletal muscle mass and/or function. Several protocols for the diagnosis of SO have been proposed in the last two decades making prevalence and disease risk estimates of SO heterogeneous and challenging to interpret. Dementia is a complex neurological disorder that significantly impacts patients, carers and healthcare systems. The identification of risk factors for early cognitive impairment and dementia is key to mitigating the forecasted trends of a 2-fold increase in dementia case numbers over the next two decades worldwide. Excess adiposity and sarcopenia have both been independently associated with risk of cognitive impairment and dementia. Whether SO is associated with a greater risk of cognitive impairment and dementia is currently uncertain. This review critically appraises the current evidence on the association between SO with cognitive outcomes and dementia risk. It also discusses some of the putative biological mechanisms that may link the SO phenotype with alteration of brain functions.

Berberine Inhibits the Disruption of the Blood-Brain Barrier and Glial Cell Activation in a Rat Model of Acute Hepatic Encephalopathy.

Hepatic encephalopathy (HE) is a complex neurological disorder in individuals with liver diseases, necessitating effective neuroprotective interventions to alleviate its adverse outcomes. Berberine (BBR), a natural compound with well-established anti-fibrotic and neuroprotective properties, has not been extensively studied in the context of glial activation under hyperammonaemic conditions. This study evaluates the neuroprotective potential of BBR in a thioacetamide (TAA)-induced HE rat model, focusing on its effects on glial activation and NLRP3 inflammasome signalling. Neurological impairments were assessed using open field tests and sensory analysis. Western blotting was performed to evaluate the expression of glial and neuronal markers, tight junction proteins and NLRP3 inflammasome components in the cortex and hippocampus. Histopathological and molecular changes were further examined using H&E, immunohistochemistry and immunofluorescence staining. BBR treatment significantly improved behavioural abnormalities and reduced systemic ammonia levels in TAA-exposed rats. It restored blood-brain barrier integrity, as evidenced by reduced tight junction protein degradation. BBR inhibited the expression of NLRP3 inflammasome markers, including caspase-1, IL-1β, ASC, and NF-κB, while reducing glial cell activation (IBA-1 and GFAP). Notably, BBR diminished NLRP3 expression in glial cells, indicating its potent anti-inflammatory effects. Additionally, BBR preserved neuronal integrity, as demonstrated by the maintained expression of MAP-2 and NeuN and reduced cleaved Gasdermin D levels. These findings suggest that BBR alleviates behavioural and molecular abnormalities in HE through NLRP3 inflammasome inhibition, highlighting its potential as a therapeutic agent for managing HE.

Serial systemic immune inflammation indices: markers of acute migraine events or indicators of persistent inflammatory status?

BackgroundMigraine is the most common complex neurological disorder, affecting over a billion people worldwide. Neurogenic inflammation has long been recognized as a key factor in the pathophysiology of migraine though little research has been directed to investigating whether inflammation is greatest in migraine with aura or without, and whether inflammation is a permanent state in migraine or whether is an event related transitory state. Thus, the primary aim of this single-centre, retrospective study was to explore the potential clinical utility of the Serial Systemic Immune-Inflammatory Indices (SSIIi) as a comparative measure of duration and severity of inflammation derived from routine blood cell counts in migraine patients with aura and no-aura both within an acute inpatient setting and as outpatients. Specifically, we assessed the role of two serial white blood cell counts to calculate the SSIIi using the formula: neutrophil count x platelet count/lymphocyte count) between aura and no-aura migraine patients at time of admission to a tertiary care centre in Melbourne, Australia, and following 24 h post admission versus comparable serial measures in 20 out patients with migraine and ongoing symptoms.Main bodyA retrospective analysis was conducted of medical records using baseline demographics and brain imaging findings from 186 migraine hospitalized in-patients who had at least two sets of white blood cell counts drawn within 24 h following their admission to the emergency department of Western Health a tertiary care center in Melbourne, Australia, over an 18-month period. Patients were categorized as having migraine with aura (MA) (N = 67) or without aura (MO) (N = 119) according to ICHD-3 criteria and compared to 2 serial measures in stable in-community acute migraineur controls (N = 20). A mixed-design ANOVA showed a significant main effect of SSIIi between patients with migraine with aura (MA) and migraine without aura (MO) during acute inpatient presentation, in comparison to a convenience sample of outpatients with migraine (MA and MO).ConclusionSSIIi levels were significantly lower in patients with migraine with aura (MA), compared to MO. MA showed a greater, though non-significant, decrease between the two measurements compared to those with migraine without aura (MO) and outpatient controls, whose SSIIi levels remained consistently higher. The control group displayed similar findings to MO inpatients, suggesting persistent systemic inflammation in a subset of migraine patients regardless of in patient or outpatient of presentation and highlighting the need for future studies to more rigorously evaluate the role of systemic inflammation in migraine pathophysiology, chronicity, and progression though the multiple phases of migraine including the interictal phase.

Clinical 7Tesla magnetic resonance imaging: Impact and patient value in neurological disorders.

Magnetic resonance imaging (MRI) is a cornerstone of non-invasive diagnostics and treatment monitoring, particularly for diseases of the central nervous system. Although 1.5- and 3Tesla (T) field strengths remain the clinical standard, the advent of 7T MRI represents a transformative step forward, offering superior spatial resolution, contrast, and sensitivity for visualizing neuroanatomy, metabolism, and function. Recent innovations, including parallel transmission and deep learning-based reconstruction, have resolved many prior technical challenges of 7T MRI, enabling its routine clinical use. This review examines the diagnostic impact, patient value, and practical considerations of 7T MRI, emphasizing its role in facilitating earlier diagnoses and improving care in conditions, such as amyotrophic lateral sclerosis (ALS), epilepsy, multiple sclerosis (MS), dementia, parkinsonism, tumors, and vascular diseases. Based on insights from over 1200 clinical scans with a second-generation 7T system, the review highlights disease-specific biomarkers such as the motor band sign in ALS and the new diagnostic markers in MS, the central vein sign, and paramagnetic rim lesions. The unparalleled ability of 7T MRI to study neurological diseases ex vivo at ultra-high resolution is also explored, offering new opportunities to understand pathophysiology and identify novel treatment targets. Additionally, the review provides a clinical perspective on patient handling and safety considerations, addressing challenges and practicalities associated with clinical 7T MRI. By bridging research and clinical practice, 7T MRI has the potential to redefine neuroimaging and advance the understanding and management of complex neurological disorders.

Outcome of Programmable Lumboperitoneal Shunt in the Surgical Management of Idiopathic Normal Pressure Hydrocephalus

Background: Normal pressure hydrocephalus (NPH) is a complex neurological disorder characterized by enlarged ventricles in the brain. While the ventriculoperitoneal shunt is the preferred procedure, lumbar-peritoneal shunts also serve as an alternative. Lumbo-peritoneal shunts are an alternative for diverting cerebrospinal fluid without intracranial surgery. Objectives: To assess the efficacy of programmable LP shunts in managing idiopathic normal-pressure hydrocephalus (iNPH), focusing on postoperative outcomes and complications. Methods: This is a multicenter cohort study, including retrospective and prospective data. It involves 20 iNPH hospitalized patients from January 1st, 2019, to January 1st, 2022. The patients underwent programmable lumboperitoneal shunt surgery and had a six-month follow-up period. Patients with confirmed diagnosis of iNPH were included in this study. Other possible causes of symptoms were ruled out, and there were no reasons why the LP shunt should not be placed. Results: This cohort study involved 20 iNPH patients treated with programmable LP shunts; 19 patients (95%) experienced gait improvement, 16(80%) showed improvement in urinary symptoms, and 16 patients (80%) showed improvement in dementia. The most common post-operative complications included over-shunting (15%), subdural hygroma, CSF collection, and infection (10%). Most patients presented between 2–4 months. When evaluating predictors of operative time, the regression analysis revealed no significant factor that could predict operative time accurately. Conclusions: Lumbar-peritoneal shunts showed significant effectiveness rates with a moderate complication rate. It is minimally invasive without life-threatening complications and can be recommended for iNPH treatment.

Personalized progression modelling and prediction in Parkinson’s disease with a novel multi-modal graph approach

Parkinson’s disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity. This study introduces an graph-based interpretable personalized progression method, utilizing data from the Parkinson’s Progression Markers Initiative (PPMI) and Stroke Parkinson’s Disease Biomarker Program (PDBP). Our approach integrates multimodal inter-individual and intra-individual data, including clinical assessments, MRI, and genetic information to make multi-dimension predictions. Validated using the PDBP dataset from 12 to 36 months, our AdaMedGraph method demonstrated strong performance, achieving AUC values of 0.748 and 0.714 for the 12-month Hoehn and Yahr Scale and Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III on the PPMI test set. Ablation analysis reveals the importance of baseline clinical assessment predictors. This novel framework improves personalized care and offers insights into unique disease trajectories in PD patients.

Fine‐mapping of existing and novel functional quantitative trait locireveals local genetic influences of epigenetic and transcriptomic regulation in human brains

AbstractBackgroundThe FunGen‐xQTL project has significantly advanced genetics by developing and exploring novel quantitative trait loci (QTL) types in human brains, enriching our understanding of complex neurological disease etiology. We broadened the scope of epigenomic QTL analysis, integrating histone acetylation QTLs (haQTLs) and methylation QTLs (mQTLs) that affect multiple histone acetylation peaks or methylation CpG sites spatially. Additionally, we investigated a new category of splicing QTLs (sQTLs) implicated in nonsense‐mediated decay (NMD).MethodUtilizing unique computational methods developed by FunGen‐xQTL for genome‐wide statistical fine‐mapping, we dissected intricate gene regulation networks at single‐base resolution, effectively accounting for linkage disequilibrium confounding.ResultOur research has unveiled complex multi‐omic regulations via fine‐mapped epigenomic QTLs using our new method called fSuSiE. We identified specific genetic variants performing dual roles as both epigenomic and eQTLs across different genes, indicative of 3D multi‐gene interactions. For instance, cis‐variant rs62324750 for TMEM131L uniquely acts as a negative eQTL, negative haQTL, and positive mQTL. This complex interaction extends to nearby genes TRIM2 and MND1, where rs11728062 impacts gene expression as a negative haQTL and positive mQTL through epigenomic markers spanning gene bodies. Oligodendrocyte‐specific Hi‐C data further support these findings, suggesting novel regulatory mechanisms involving disruption of transcriptional repressor bindings. Moreover, neuronal Hi‐C and ChiP‐Seq data uncover a unique xQTL interaction pattern in APOL2, indicative of 3D protein‐chromatin structures in transcription regulation.Employing LeafCutter2 methods from FunGen‐xQTL, we investigated the genetic basis of NMD by identifying premature termination codons from RNA‐seq data and fine‐map the corresponding sQTL for such unproductive splicing events. In the ROSMAP DLPFC dataset, we distinguished 45,999 unproductive from 130,664 productive splicing events for NMD. We observed a higher colocalization ratio of unproductive sQTLs with eQTLs, illuminating new insights into AD etiology. Notably, an unproductive sQTL in TSPAN14, colocalized with an AD GWAS signal, revealed significant gene expression and intron usage differences across genotypes, suggesting RNA expression of TSPAN14 may be mediated by genetically regulated NMD events.ConclusionOur work demonstrates the complex interplay of novel QTL types in gene regulation and offers significant insights for future research into the genetic mechanisms of complex diseases.

Application of Brain-Computer Interface Technology in Neurorehabilitation

Abstract. Neurorehabilitation is a very important area that aims at bettering lives of patients who have neurological disorders and have severely damaged motor functions and impaired communications abilities. Brain-Computer Interface (BCI) technology has emerged as one of the highly promising tools in the application of neurorehabilitation, having innovations in motor recovery, speech restoration, and independence for patients. This review presents the applications and efficiency of brain-computer interface technology in neurorehabilitation procedures related to high-incidence neurological disorders like stroke, traumatic brain injury, and spinal cord injury, and complex neurological disorders like amyotrophic lateral sclerosis, multiple sclerosis, and cerebral palsy. Among these, some BCI approaches, mostly based on electroencephalographic recordings, have shown promising potential for motor recovery, communication, and improvement of independence in patients. It discusses key modalities like motor imagery-based training, neurofeedback, and robotic assistance, together with the landmark studies proving their efficacy. Moreover, it pays attention to basic and clinical research and points out the challenges and future directions for the alleviation of limitations in BCI technology. Despite the fact that BCI technology still has some basic problems at this point in terms of signal acquisition, processing, and individual training, there is huge potential for application in developing neurorehabilitation and improving the quality of life of those patients who have undergone serious neurological injury.

ESTUDO COMPARATIVO DOS EFEITOS COLATERAIS DO CANABIDIOL E OUTRAS DROGAS NO TRATAMENTO DE EPILEPSIA DO TIPO SÍNDROME DE DRAVET

Introduction: Dravet Syndrome is a complex neurological disorder that begins in the first months of life due to a mutation in the SCN1A gene. This syndrome is pharmacoresistant and requires specific medications to prevent worsening in the development of these children. Cannabidiol is a phytocannabinoid extracted from the Cannabis sativa plant that acts as a modulator in the endocannabinoid system. Objective: To compare conventional drugs with cannabidiol, their side effects, and the quality of life of patients after using cannabidiol. Method: This is an integrative literature review. The sources used were PubMed, ScienceDirect, and SCIELO. The searches were conducted using the descriptors: Cannabidiol, Dravet Syndrome, epilepsy, SCN1A gene mutation, CBD efficacy, mechanism of action, conventional drugs. Results: Of the 64 selected articles, 26 reported a significant reduction in seizure frequency; 6 did not yield favorable results regarding the use of cannabidiol in epilepsy treatment; 12 showed long-term efficacy of cannabidiol use; 12 reported benefits of cannabidiol use in children with Dravet Syndrome; and 8 indicated an improvement in quality of life. Conclusion: There is significant progress that cannabidiol has brought to patients with Dravet Syndrome, improving the quality of life for both patients and their caregivers. However, more studies, especially long-term, need to be conducted.

A review of migraine treatment methods: a comparative analysis of therapeutic approaches

Migraine, a complex neurological disorder, affects a significant portion of the population and presents both medical and societal challenges. The aim of this paper is to provide a detailed review and analysis of available therapeutic methods for patients suffering from migraines, considering both pharmacological and non-pharmacological therapies. The approaches presented include acute treatment with triptans and nonsteroidal anti-inflammatory drugs (NSAIDs), as well as preventive therapies, such as beta-blockers and antiepileptic drugs. The section on non-pharmacological therapies focuses on cognitive-behavioral therapy (CBT), biofeedback, and lifestyle recommendations, including diet and sleep hygiene. A review of the literature indicates that integrating pharmacological and non-pharmacological approaches can significantly improve treatment effectiveness and the quality of life for patients with migraines.

“Brain Biopsy Revolution: Unveiling the Core Syringe Technique With Clinical Insights”

BackgroundObtaining a definitive pathological diagnosis from brain tissue sampling was challenging due to the small, non-representative sample. This study introduced a novel syringe technique for brain biopsy aimed at enhancing diagnostic accuracy by obtaining core tissue samples that better represent the targeted tissue. MethodsThe ten patients with atypical brain lesions underwent the syringe biopsy. After meticulous preoperative planning with neuronavigation, a minimally invasive approach was used: a 3 cm skin incision and a 14 mm burr hole were created. A modified 3-cc syringe was used to create negative pressure and cannulate the brain tissue. The desired sample size (24 cm³) was obtained by controlling the syringe depth and withdrawal. Medical records were reviewed to assess sample analysis results and any complications. ResultsThe syringe technique successfully yielded adequate tissue samples in 9 out of 10 patients. In one case, the desired tissue could not be retrieved and required a microsurgical approach for removal. In all ten cases, a correct diagnosis was made without significant complications. ConclusionThe preliminary findings suggest that the syringe technique is both safe and effective for obtaining substantial volumes of brain tissue, facilitating accurate pathological evaluation in cases of complex neurological disorders.

A Beginner's Guide to Genomics in Complex Neurological Disorders

In view of the recent progress in genomics and next-generation sequencing technologies, we consider here their contribution toward neurological disorders. We describe the revolutionary impact that whole-genome and whole-exome sequencing have had on diagnostics and the identification of important genetic loci associated with a range of disorders. Neurological disorders affecting the central and peripheral nervous systems are complex, both in diagnosis and treatment. Herein we show that by applying multiomic tools to assist researchers in identifying new biomarkers and therapeutic approaches, the diagnosis, treatment, and prognosis of these disorders can be significantly improved.

Deciphering the Symptom Spectrum: A Comprehensive Analysis of Migraine Patterns and Types

Complex neurological diseases like migraine migraine affect a large section of the global population, causing health, social, and economic issues. Migraine causes intense, painful headaches that are usually one-sided and pulsing. Auras, nausea, vomiting, and excessive light and sound sensitivity may precede these episodes. Migraine affect millions worldwide and can be intermittent or persistent, impairing function. Diet and stress may induce it, but the cause is unknown. Prevention and symptom treatment drugs and lifestyle changes are used. Debilitating migraines are hard to diagnose due to their varied presentation and subjective symptom reporting. Traditional migraine diagnosis, based on clinical evaluation, typically fails to classify migraine types, requiring more objective and rigorous instruments. This study proposes a machine learning-based migraine categorization method to address this issue. The dataset includes different patient demographics and clinical variables; thus, we use complex algorithms like Random for Forest, XGBoost, and Extra Trees. These algorithms are great for deciphering migraine patterns because they excel at evaluating complex datasets. The research seeks to close this gap to improve migraine classification accuracy, objectivity, and reliability, enabling tailored migraine management and treatment. This neurology study could im- prove migraine diagnosis and treatment with more effective and personalized plans.

Evaluating the Clinical Validity and Reliability of Artificial Intelligence-Enabled Diagnostic Tools in Neuropsychiatric Disorders.

Neuropsychiatric disorders (NPDs) pose a substantial burden on the healthcare system. The major challenge in diagnosing NPDs is the subjective assessment by the physician which can lead to inaccurate and delayed diagnosis. Recent studies have depicted that the integration of artificial intelligence (AI) in neuropsychiatry could potentially revolutionize the field by precisely diagnosing complex neurological and mental health disorders in a timely fashion and providing individualized management strategies. In this narrative review, the authors have examined the current status of AI tools in assessing neuropsychiatric disorders and evaluated their validity and reliability in the existing literature. The analysis of various datasets including MRI scans, EEG, facial expressions, social media posts, texts, and laboratory samples in the accurate diagnosis of neuropsychiatric conditions using machine learning has been profoundly explored in this article. The recent trials and tribulations in various neuropsychiatric disorders encouraging future scope in the utility and application of AI have been discussed. Overall machine learning has proved to be feasible and applicable in the field of neuropsychiatry and it is about time that research translates to clinical settings for favorable patient outcomes. Future trials should focus on presenting higher quality evidence for superior adaptability and establish guidelines for healthcare providers to maintain standards.

Neuropsychiatric Manifestations of Degenerative Cerebellar Ataxia.

Degenerative cerebellar ataxias (DCA) present a group of complex neurological disorders primarily affecting the cerebellum and its pathways. Classic manifestations include motor symptoms of cerebellar ataxia. However, emerging evidence suggests that the cerebellum also plays a crucial role in various cognitive and emotional processes. The objective was to assess the psychiatric profile of a heterogeneous group of patients with degenerative cerebellar ataxia. Our sample comprised 107 participants diagnosed with cerebellar degenerative ataxia. All patients were clinically evaluated using SARA, INAS, and different neuropsychiatric scales (ACE-R, HAMA, HAMD, AS, and GAF). The majority of patients had autosomal dominant ataxia (38.3%) followed by sporadic ataxia (32.7%) with an average age at the moment of diagnosis of 35.3 ± 16.23 years, while the mean duration of disease at the study beginning was 12.1 ± 9.9 years. Psychiatric disorders were present in 40 patients (37.4%), with dysthymia (14.2%), major depressive disorder (9.4%), and MDD with melancholic features (7.6%). The presence of MDD with melancholic features was statistically significantly correlated with a lower ACE-R total score (r = -0.223; p = 0.022), while dysthymia was statistically significantly associated with a shorter duration of the disease (r = -0.226; p = 0.020) and older age (r = 0.197; p = 0.043). Statistically significant differences were observed between MSA-C patients and those with sporadic ataxia (HDRS p < 0.001, HARS p < 0.001, Apathy Scale p = 0.003, and GAF p = 0.004). Based on our findings, we can conclude that the degree of motor deficit has a significant impact on the development of psychiatric disorders, including depression, anxiety, and apathy. However, it is not the only factor, and the impact also depends on the type of DCA.

Brain imaging traits and epilepsy: Unraveling causal links via mendelian randomization.

Epilepsy, a complex neurological disorder, is closely linked with structural and functional irregularities in the brain. However, the causal relationship between brain imaging-derived phenotypes (IDPs) and epilepsy remains unclear. This study aimed to investigate this relationship by employing a two-sample bidirectional Mendelian randomization (MR) approach. The analysis involved 3935 cerebral IDPs from the UK Biobank and all documented cases of epilepsy (all epilepsies) cohorts from the International League Against Epilepsy, with further validation through replication and meta-analyses using epilepsy Genome-Wide Association Studies datasets from the FinnGen database. Additionally, a multivariate MR analysis framework was utilized to assess the direct impact of IDPs on all epilepsies. Furthermore, we performed a bidirectional MR analysis to investigate the relationship between the IDPs identified in all epilepsies and the 15 specific subtypes of epilepsy. The study identified significant causal links between four IDPs and epilepsy risk. Decreased fractional anisotropy in the left inferior longitudinal fasciculus was associated with a higher risk of epilepsy (odds ratio [OR]: 0.89, p=3.31×10-5). Conversely, increased mean L1 in the left posterior thalamic radiation (PTR) was independently associated with a heightened epilepsy risk (OR: 1.14, p=4.72×10-5). Elevated L3 in the left cingulate gyrus was also linked to an increased risk (OR: 1.09, p=.03), while decreased intracellular volume fraction in the corpus callosum was correlated with higher epilepsy risk (OR: 0.94, p=1.15×10-4). Subtype analysis revealed that three of these IDPs are primarily associated with focal epilepsy (FE). Notably, increased L1 in the left PTR was linked to an elevated risk of hippocampal sclerosis (HS) and lesion-negative FE, whereas elevated L3 in the left cingulate gyrus was associated with HS-related FE. Our research offers genetic evidence for a causal link between brain IDPs and epilepsy. These results enhance our understanding of the structural brain changes associated with the onset and progression of epilepsy.

A Case Report on Anti-N-methyl-d-aspartate Receptor Encephalitis in a 52-year-old Female with Schizophrenia

Abstract Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is a complex neurological and psychiatric disorder. It often presents with psychiatric symptoms, leading to misdiagnosis, but can progress to severe neurological manifestations. In this study, we presented a 52-year-old woman with a 30-year history of schizophrenia who suddenly developed mutism, agitation, and incontinence. Electroencephalography showed generalized slowing, and autoimmune testing revealed NMDAR antibody positivity. Treatment with intravenous methylprednisone led to rapid improvement, highlighting the importance of considering autoimmune causes in such cases. NMDAR encephalitis is a condition with both neurological and psychiatric symptoms. It is often initially mistaken for psychiatric issues. Physicians should carefully assess and be vigilant in psychiatric cases. If routine tests show no significant issues, but the neurological state worsens, considers NMDAR encephalitis, and acts promptly. Delayed treatment can be harmful.

EEG-based epileptic seizure detection using deep learning techniques: A survey

Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and spatial shifts in cerebral electrical activity, holding pivotal diagnostic and therapeutic value for epilepsy. Deep learning techniques have made remarkable progress in EEG-based seizure detection over recent years. This review is dedicated to exploring seizure detection approaches based on deep learning, focusing on three distinct avenues. Primarily, we delve into the application of canonical deep learning methods in epilepsy detection. Subsequently, a more in-depth study was conducted on the hybrid models of deep learning. Next, the third is the integration of deep learning and traditional machine learning strategies. Finally, the challenges and future prospects related to this topic are put forward. The uniqueness of this review lies in its novel and comprehensive perspective on the latest research on deep learning-based epilepsy detection by systematically classifying methods, visualizing research progress, and addressing challenges and gaps in current research. It can provide valuable guidance for researchers who want to delve into the field of epileptic seizure detection based on EEG signals.

Aducanumab in Alzheimer's Disease: A Critical Update.

Alzheimer's disease (AD) is a complex neurological disorder that results in cognitive decline. The incidence rates of AD have been increasing, particularly among individuals 60 years of age or older. In June 2021, the US FDA approved aducanumab, the first humanized monoclonal antibody, as a potential therapeutic option for AD. Clinical trials have shown this drug to effectively target the accumulation of Aβ (beta-amyloid) plaques in the brain, and its effectiveness is dependent on the dosage and duration of treatment. Additionally, aducanumab has been associated with improvements in cognitive function. Biogen, the pharmaceutical company responsible for developing and marketing aducanumab, has positioned it as a potential breakthrough for treating cerebral damage in AD. However, the drug has raised concerns due to its high cost, limitations, and potential side effects. AD is a progressive neurological condition that affects memory, cognitive function, and behaviour. It significantly impacts the quality of life of patients and caregivers and strains healthcare systems. Ongoing research focuses on developing disease-modifying therapies that can halt or slow down AD progression. The pathogenesis of AD involves various molecular cascades and signaling pathways. However, the formation of extracellular amyloid plaques is considered a critical mechanism driving the development and progression of the disease. Aducanumab, as a monoclonal antibody, has shown promising results in inhibiting amyloid plaque formation, which is the primary pathological feature of AD. This review explores the signaling pathways and molecular mechanisms through which aducanumab effectively prevents disease pathogenesis in AD.

A critical appraisal of geroprotective activities of flavonoids in terms of their bio-accessibility and polypharmacology

Flavonoids, a commonly consumed natural product, elicit health-benefits such as antioxidant, anti-inflammatory, antiviral, anti-allergic, hepatoprotective, anti-carcinogenic and neuroprotective activities. Several studies have reported the beneficial role of flavonoids in improving memory, learning, and cognition in clinical settings. Their mechanism of action is mediated through the modulation of multiple signalling cascades. This polypharmacology makes them an attractive natural scaffold for designing and developing new effective therapeutics for complex neurological disorders like Alzheimer's disease and Parkinson's disease. Flavonoids are shown to inhibit crucial targets related to neurodegenerative disorders (NDDs), including acetylcholinesterase, butyrylcholinesterase, β-secretase, γ-secretase, α-synuclein, Aβ protein aggregation and neurofibrillary tangles formation. Conserved neuro-signalling pathways related to neurotransmitter biogenesis and inactivation, ease of genetic manipulation and tractability, cost-effectiveness, and their short lifespan make Caenorhabditis elegans one of the most frequently used models in neuroscience research and high-throughput drug screening for neurodegenerative disorders. Here, we critically appraise the neuroprotective activities of different flavonoids based on clinical trials and epidemiological data. This review provides critical insights into the absorption, metabolism, and tissue distribution of various classes of flavonoids, as well as detailed mechanisms of the observed neuroprotective activities at the molecular level, to rationalize the clinical data. We further extend the review to critically evaluate the scope of flavonoids in the disease management of neurodegenerative disorders and review the suitability of C. elegans as a model organism to study the neuroprotective efficacy of flavonoids and natural products.