搜索结果：Neurology (E-Cronicon)

Addictive behaviors-including the misuse of tobacco, alcohol, illicit substances, and compulsive activities such as gambling, overeating, and sexual excess-remain widespread and profoundly burdensome on both individuals and societies. Their impact is multifaceted, encompassing adverse health outcomes, increased criminal activity, and significant economic costs due to lost productivity. Addiction is a highly complex condition shaped by genetic predispositions, environmental influences, and behavioral factors, all of which contribute to disruptions in neural regulation and compulsive decision-making. While advances have been made in identifying the genetic and biochemical underpinnings of addiction and related psychiatric disorders, progress in developing broadly effective therapies has been limited. Addiction is often characterized by feelings of fragmentation, helplessness, and existential despair-phenomena that may reflect a deeper, unmet need for personal integration, purpose, and transformation. This perspective supports the notion that spiritual yearning can be an integral part of the recovery process. Efforts to address addiction have frequently overlooked the potential therapeutic value of spirituality in fostering healing. If one accepts the premise that the brain governs both conscious and unconscious experience-including religious and spiritual phenomena-it follows that addiction, and mental illness may involve disrupted neural systems that regulate reward and suffering. We propose that individuals may have differing capacities for spiritual resilience or growth based on a dynamic interplay between their genetic architecture and epigenetic factors (e.g. life experiences, trauma, social environment). This expert opinion presents current evidence supporting this hypothesis and highlights the relevance of the Hierarchical Neuro-Spiritual Model (HNSM) as a novel framework for understanding the spiritual dimension in the pathophysiology and treatment of addiction.

Dopaminergic dysfunction in reward circuitry is well-documented as a contributor to addictive behaviors. Evidence indicates that changes in synchronous neural activity between brain regions mediating reward and cognitive functions may significantly contribute to substance-related disorders. In this commentary we highlight findings showing that the pro-dopaminergic nutraceutical (KB220) enhances functional connectivity between reward and cognitive brain areas in both animal and human studies. Animal studies demonstrate that KB220 activates important brain reward-related regions, including the nucleus accumbens, anterior cingulate gyrus, anterior thalamic nuclei, hippocampus, and prelimbic and infralimbic loci. Kb220 induced significant functional connectivity, enhanced neuroplasticity, and improved dopaminergic functionality within the brain reward circuitry with effects localized to these regions rather than broader distributed across the brain. In abstinent heroin-dependent individuals, acute KB220 administration significantly induced BOLD activation in caudate-accumbens dopaminergic pathways relative to placebo. Furthermore, data from 36 clinical trials and preclinical studies encompassing over 1,000 subjects, demonstrate that KB220 supports "dopamine homeostasis" across various reward deficiency behaviors. Clinical outcomes and quantitative electroencephalogy (qEEG) results underscore KB220's potential anti-craving/anti-relapse effects in addiction and other psychiatric disorders through direct or indirect dopaminergic modulation. Based on a review of the existing knowledge and further intensive investigation, we propose that instead of relying on mono-pharmaceutical approaches, the scientific community should endorse multi-loci dopaminergic restoration of reward brain circuitry as a fundamental paradigm for addressing mental illness.

Myalgic Encephalomyelitis (ME) and Chronic Fatigue Syndrome (CFS) are stigmatizing illnesses characterized by cognitive difficulties, post-exertional malaise, unrefreshing sleep, and other symptoms. Patients are often incapacitated and stigmatized as having a psychological disorder. The Chronic Fatigue Attitudes Test (CAT) assesses stigmatizing views toward individuals with Chronic Fatigue Syndrome, however, there is little research examining factors that may account for variation in stigmatizing attitudes toward this group. We examined CAT scores among college age research volunteers (N = 90), hypothesizing that exposure to information about ME and CFS as a result of volunteering on a ME and CFS-related research project would be associated with less stigmatizing attitudes compared to volunteers on unrelated projects. Findings indicated that ME and CFS research volunteers expressed less stigmatizing attitudes. Educational efforts aiming to disseminate accurate information about ME and CFS may mitigate stigma and the experience of stigma among individuals with ME and CFS.

Few studies have explored the rate of cognitive decline and caregiver burden within the context of a specialized memory clinic. When this was done, the focus was largely on functional decline related to Alzheimer's disease (AD). Our goal was to compare the longitudinal decline of AD patients to those with Vascular Dementia (VaD) on Mini-Mental State Examination (MMSE). We further explored the differential impact on caregiver burden. We retrospectively studied 237 charts from patients seen at our Memory Clinic between 2006 and 2012. The data was collected over 17 years. Cohorts were formed by excluding conditions other than AD and VaD, and including patients who had been assessed at least twice with the MMSE (AD: n = 83; mean age: 67.7 yo; VaD: n = 32; mean age: 73.3yo). A small group of 36 caregivers was surveyed by phone to explore caregiver burden. Results indicated that the natural history of MMSE changes in AD patients differed significantly from that of patients with VaD (F = 10.41, p<0.0014), with AD patients showing more cognitive decline over time. Sadness, stress/anxiety, fatigue, and sleep disorders were reported as the main preoccupations by caregivers and its impact was rated as 'severe' in 50% of cases. Altogether, this study provides further insight into the natural history of cognitive decline in AD and VaD. Future studies should explore the progression of dementing disorders in larger cohorts using prospective methodological designs.

The Hoffman-reflex (H-reflex) is an electrophysiological technique used to evaluate the excitability of the monosynaptic spinal reflex arc. In individuals with upper motor neuron lesions who show elevated spinal excitability, a depression of spinal excitability may indicate adaptive spinal plasticity. Downslope walking (DSW), an exercise intervention comprising repetitive eccentric muscle activity, has been shown to induce depression of soleus H-reflex amplitudes while seated, however, the dose-response time-course of H-reflex modulation during DSW has not been characterized. The objectives of this study were twofold: (1) to evaluate DSW-induced soleus H-reflex depression in the standing posture and during walking, and (2) to investigate the effect of walking duration (20 minutes and 40 minutes) of DSW (-15% decline) on soleus H-reflexes, (with level walking (LW) as a control intervention). Soleus H-reflexes were collected Pre, Post-20 minutes, and Post-40 minutes of walking in the standing position; and H-reflexes were also measured at 4 different time points during the terminal stance phase of walking. Our results showed that soleus H-reflexes evaluated in standing showed a greater % depression after DSW compared to LW, with a statistical trend for greater depression with longer durations (40-minutes). H-reflexes measured during walking showed greater depression after 40 minutes of walking compared to 20- or 30-minutes for both DSW and LW. Longer duration treadmill walking (40-minutes) may induce a greater acute depressive effect on soleus H-reflex excitability compared to shorter durations (20-minutes) of treadmill walking. Future work will investigate the potential for DSW as a gait training intervention in people with upper motor neuron lesions such as multiple sclerosis and stroke.

Veterans and patients with epilepsy are at higher risk of suicide than the general population. Some studies suggest that antiepileptic drugs (AEDs) further increase the risk of suicide. The nature of the relationship between suicidality and epilepsy treatment needs clarification. We examined this relationship in a cohort of veterans with seizures. We performed a retrospective chart analysis of patients at the Philadelphia VA Medical Center with a diagnosis of seizure disorder between January 2000 and April 2007. Patients with suicidal ideation and/or suicidal behaviors were analyzed with respect to the following risk factors: age, history of traumatic brain injury (TBI), substance abuse and AED prescription. 526 charts were reviewed, 385 of which met inclusion criteria. Patients with substance abuse were more likely to have suicidal ideation (adjusted odds ratio 3.37, 95% CI 1.84 -6.18). Risk decreased with age (adjusted odds ratio 0.94, 95% CI 0.92 - 0.97 for each year). There was no statistically significant relationship between suicidality and AED use or history of TBI. In our population, AEDs were not associated with increased risk of suicidality, whereas substance abuse was associated with a substantial risk increase. The interactions among seizures, suicidality, substance abuse and other neuropsychiatric diseases are complex. Large-scale studies in patients with seizures are needed to understand the impact of individual drugs and other contributing factors. Providers should be cautious not to withhold potentially beneficial treatment, however patients with risk factors such as history of substance abuse should be followed closely after AED initiation or adjustment.

It is unclear what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS). The current study compared self-report symptom data of patients with ME or CFS with those with MS. The self-report data is from the DePaul Symptom Questionnaire, and participants were recruited to take the questionnaire online. Data were analyzed using a machine learning technique called decision trees. Five symptoms best differentiated the groups. The best discriminating symptoms were from the immune domain (i.e., flu-like symptoms and tender lymph nodes), and the trees correctly categorized MS from ME or CFS 81.2% of the time, with those with ME or CFS having more severe symptoms. Our findings support the use of machine learning to further explore the unique nature of these different chronic diseases.

Plagiocephaly is a common condition that affects infants. It can be broadly grouped into positional and non positional plagiocephaly Positional plagiocephaly frequently resolves without intervention. Non positional plagiocephaly resulting from craniosynostosis often requires surgical intervention. In this case report, we present a rare case of unilateral frontosphenoid craniosynostosis. We discuss the appropriate diagnostic workup, the available treatment options, and patient follow-up over time. Furthermore, we provide a detailed review of the literature discussing treatment options for aesthetic appearance as the child ages.

The gut microbiome appears to be predictive of Parkinson's disease (PD) with constipation. Chronic constipation frequently manifests prior to motor symptoms and impairs quality of life. An osteopathic manipulative medicine (OMM) sequence used physical exam assessment and manual treatment of neuromusculoskeletal dysfunctions pertinent to constipation in PD for this prospective ABA-design study, IRB-NYITBHS1065. The effects of 4 weekly treatments on the gut microbiome among men and women over 40 years old with chronic constipation and PD were investigated. Severity of PD was rated with the Movement Disorders Society-Unified PD rating scale (UPDRS) in six subjects with constipation. Also, the Bristol stool scale and questionnaires validated for constipation were administered for diagnosis, symptom severity, and quality of life during a 4-week control-period (A), 4-weekly OMM-treatments (B), and 2-weeks no-intervention (A). Biweekly stool samples were assessed for normalized microbiota abundance. The mean Bristol rating improved from type 2 (&#xb1; 1) Pre-OMM to 3 (&#xb1; 1; p = .167; d = 0.677) Post-OMM. Mean constipation severity significantly decreased (p = .010; d = 1.508) Post-OMM. Mean quality of life significantly improved (p = .041; d = 1.072) Post-OMM. The Pre-OMM mean number of families within the phylum Firmicutes decreased by 3 (p = .043; d = 1.177) Post-OMM. There were significant changes in the normalized abundance of phyla Actinobacteria (p = .040; d = 0.845) and Verrucomicrobia (p = .024; d = 0.675) as well as in genus Roseburia (p = .033; d = 1.109), Intestinimonas (p = .035; d = 0.627) and Anaerotruncus (p = .004) Post-OMM. The gut microbiome shifted among individuals with constipation and PD after four weekly treatments with the OMM-sequence. Changes in the gut microbiome Post-OMM were associated with UPDRS results and constipation measures. Clinical trials and studies to develop the gut microbiome into a validated biomarker for PD are necessary to understand the impact of OMM in patients with PD and constipation.

Neurodegenerative diseases demonstrate the progressive decline of brain functions resulting in a significant deterioration in the quality of patient's life. With increasing life expectancy, there has been a significant increase in the incidence of these diseases. Neurodegenerative diseases like Alzheimer's, Parkinson's, and Amyotrophic lateral sclerosis are devastating and afflicts a large world population. Eye, given the similar neural and vascular similarity to the brain, demonstrates many pathological hallmarks of some of these neurological diseases. Moreover, these diseases create an economic and social burden to society. Despite tremendous efforts made in the drug discovery, there is no cure for these fatal diseases. Thus, there is an unmet need to understand cellular and molecular pathophysiology of these diseases. All these diseases demonstrate damage to a large number of seemingly disparate cellular processes and functions such as Ca+2 homeostasis, lipid metabolism, axonal transport, unfolded protein response, autophagy and inflammatory responses. Mitochondria are closely associated with Endoplasmic reticulum (ER) and ER-mitochondrial cross-talk regulates many of these cellular processes and functions damaged in neurodegenerative and eye diseases. Several studies have implicated the disruption of ER-mitochondria contacts in these diseases. This review is aimed at understanding and summarizing the role of ER-mitochondria interacting proteins in major neurodegenerative and eye diseases studied so far.

The Institute of Medicine (IOM) recently developed clinical criteria for chronic fatigue syndrome (CFS). There might be additional criteria that could select a more homogenous and impaired group of patients, particularly those with pain. The current study focused on criteria which involved meeting the four IOM criteria, excluding medical and psychiatric co-morbidities, along with having fibromyalgia (FM). Findings indicated that those meeting the IOM clinical criteria plus FM were more impaired on a wide variety of symptoms and functional areas than those meeting on the IOM criteria or those with just 6 months of fatigue. The implications of using such research criteria are discussed.

Many US states now embrace the medical and recreational use of Cannabis. Changes in the laws have heightened interest and encouraged research into both cannabinoid products and the potential harms of Cannabis use, addiction and intoxication. The major active ingredient of Cannabis sativa (marijuana), &#x394;9-tetrahydrocannabinol (THC) and it powerfully stimulates the type-1 cannabinoid (CB1) receptor. When used in the form of the plant marijuana, because of the many compounds that exist in the plant form they could inhibit the activity of the CB1 receptor thereby reducing many of the effects of THC. While this mechanism seems correct, in our opinion, Vallee., et al. incorrectly suggest that blocking CB1 receptors could open unforeseen approaches to the treatment of cannabis intoxication and addiction. We caution the scientific community that, other CB1 receptor blockers, such as, Rimonabant (SR141718) have been pulled off the market in Europe. In addition, CB1 receptor blockers were rejected by the FDA due to mood changes including suicide ideation. We argue that one issue facing the scientific community, has to do with the increasing legalization of Cannabis products in many states across America. We are in favor of some reform in terms of either decriminalization or restrictive legalization especially in control of legal limits of THC. Like other psychoactive compounds at high doses, it is our hypothesis that chronic use of these drugs including high THC content in its various forms (wax, smoke or vapor) resulting in brain reward dysfunction induces an imbalance of neurotransmission and subsequent hypodopaminergia and lead to aberrant substance and non-substance (behavioral) addictions. It is further proposed that in order to overcome THC and even other psychoactive drugs of abuse induced anhedonia the coupling of genetic risk testing and pro dopamine regulation is warranted.

Robotic technology has the potential to revolutionize the field of neurology by providing new methods for diagnosis, treatment, and rehabilitation of neurological disorders. In recent years, there has been an increasing interest in the development of robotics applications for neurology, driven by advances in sensing, actuation, and control systems. This review paper provides a comprehensive overview of the recent advancements in robotics technology for neurology, with a focus on three main areas: diagnosis, treatment, and rehabilitation. In the area of diagnosis, robotics has been used for developing new imaging techniques and tools for more accurate and non-invasive mapping of brain structures and functions. For treatment, robotics has been used for developing minimally invasive surgical procedures, including stereotactic and endoscopic approaches, as well as for the delivery of therapeutic agents to specific targets in the brain. In rehabilitation, robotics has been used for developing assistive devices and platforms for motor and cognitive training of patients with neurological disorders. The paper also discusses the challenges and limitations of current robotics technology for

Accurate diagnosis of neurological disorders is contingent upon advanced imaging modalities such as Magnetic Resonance Imaging (MRI), which commonly utilize sparse imaging techniques to reconstruct images from limited data, thus reducing storage and acquisition time. However, challenges remain in managing noise and preserving critical diagnostic features for effective analysis. In this study, an ensemble classifier is enriched with PARAFAC CP tensor decompositions, drawing mathematical inspiration from quantum neural network architectures but implemented entirely classically. The model was evaluated on a large, balanced clinical dataset comprising 55,160 images across 8 diagnostic categories, employing both higher and lower PARAFAC rank configurations. Evaluated through 5-fold nested stratified cross-validation, both configurations achieved strong validation performance, demonstrating robustness to tensor network expressivity. Additionally, the proposed model achieved competitive performance relative to recent classical approaches, further underscoring the potential of quantum-inspired classical frameworks to enhance medical image analysis and support reliable clinical diagnosis. F

Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning. Mechanistic models provide valuable scientific insight into the disorders, but in practice they are often simplified with assumptions or computationally expensive and slow to solve. However, while purely data driven approaches provide speed and scalability, they require large, high quality data to train and generally suffer from interpretability and generalization issues. This perspective paper presents a structured overview of hybrid modeling strategies, which combine deep learning models with physics based solvers, and are categorized into parallel, series, and parallel-series architectures. Three main approaches that have been emphasized are residual modeling for missing or incomplete physics, Neural Ordinary Differential Equations (NODEs) for continuous time dynamics approximation, and solver in the loop that accelerates traditional solvers with neural approximations. These hybrid models integrate the governing differential equation based formulations and deep learning to characteriz

Generation of automated clinical notes have been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models. We fine tuned BERT and BART models and optimized for factuality through constraining beam search, which we trained and tested using EHR data from patients admitted to the neurology unit of an academic medical center. The approach demonstrated good ROUGE scores with an R-2 of 13.76. In a blind evaluation, two board-certified physicians rated 62% of the automated summaries as meeting the standard of care, which suggests the method may be useful clinically. To our knowledge, this study is among the first to demonstrate an automated method for generating a discharge summary hospital course that approaches a quality level of what a physician would write.

Abnormal head movements (AHMs) manifest across a broad spectrum of neurological disorders; however, the absence of a multi-condition resource integrating kinematic measurements, clinical severity scores, and patient demographics constitutes a persistent barrier to the development of AI-driven diagnostic tools. To address this gap, this study introduces NeuroPose-AHM, a knowledge-based dataset of neurologically induced AHMs constructed through a multi-LLM extraction framework applied to 1,430 peer-reviewed publications. The dataset contains 2,756 patient-group-level records spanning 57 neurological conditions, derived from 846 AHM-relevant papers. Inter-LLM reliability analysis confirms robust extraction performance, with study-level classification achieving strong agreement (kappa = 0.822). To demonstrate the dataset's analytical utility, a four-task framework is applied to cervical dystonia (CD), the condition most directly defined by pathological head movement. First, Task 1 performs multi-label AHM type classification (F1 = 0.856). Task 2 constructs the Head-Neck Severity Index (HNSI), a unified metric that normalizes heterogeneous clinical rating scales. The clinical relevance

This report documents the development and evaluation of domain-specific language models for neurology. Initially focused on building a bespoke model, the project adapted to rapid advances in open-source and commercial medical LLMs, shifting toward leveraging retrieval-augmented generation (RAG) and representational models for secure, local deployment. Key contributions include the creation of neurology-specific datasets (case reports, QA sets, textbook-derived data), tools for multi-word expression extraction, and graph-based analyses of medical terminology. The project also produced scripts and Docker containers for local hosting. Performance metrics and graph community results are reported, with future possible work open for multimodal models using open-source architectures like phi-4.

Background: A large number of neurology case reports have been published, but it is a challenging task for human medical experts to explore all of these publications. Text mining offers a computational approach to investigate neurology literature and capture meaningful patterns. The overarching goal of this study is to provide a new perspective on case reports of neurological disease and syndrome analysis over the last six decades using text mining. Methods: We extracted diseases and syndromes (DsSs) from more than 65,000 neurology case reports from 66 journals in PubMed over the last six decades from 1955 to 2017. Text mining was applied to reports on the detected DsSs to investigate high-frequency DsSs, categorize them, and explore the linear trends over the 63-year time frame. Results: The text mining methods explored high-frequency neurologic DsSs and their trends and the relationships between them from 1955 to 2017. We detected more than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While the trend analysis showed the increasing trends in the case reports for top-10 high-frequency DsSs, the categories had mixed trends. Conclusion: Our study provided new insigh