Magnetic resonance-guided focused ultrasound unilateral thalamotomy for medically refractory essential tremor: 3-year follow-up data.

Introduction: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy of the ventralis intermediate (Vim) nucleus is an "incisionless" treatment for medically refractory essential tremor (ET). We present data on 49 consecutive cases of MRgFUS Vim thalamotomy followed-up for 3 years and review the literature on studies with longer follow-up data. Methods: A retrospective chart review of patients who underwent MRgFUS thalamotomy (January 2018-December 2020) at our institution was performed. Clinical Rating Scale for Tremor (CRST) and Quality of Life in Essential Tremor (QUEST) scores were obtained pre-operatively and at each follow-up with an assessment of side effects. Patients had post-operative magnetic resonance imaging within 24 h and at 1 month to figure out lesion location, size, and extent. The results of studies with follow-up ≥3 years were summarized through a literature review. Results: The CRST total (baseline: 58.6 ± 17.1, 3-year: 40.8 ± 18.0) and subscale scores (A + B, baseline: 23.5 ± 6.3, 3-year: 12.8 ± 7.9; C, baseline: 12.7 ± 4.3, 3-year: 5.8 ± 3.9) and the QUEST score (baseline: 38.0 ± 14.8, 3-year: 18.7 ± 13.3) showed significant improvement that was stable during the 3-year follow-up. Three patients reported tremor recurrence and two were satisfactorily retreated. Side effects were reported by 44% of patients (severe: 4%, mild and transient: 40%). The improvement in tremor and quality of life in our cohort was consistent with the literature. Conclusion: We confirmed the effectiveness and safety of MRgFUS Vim thalamotomy in medically refractory ET up to 3 years.

Long-term effects of SARS-CoV-2 infection in patients with and without chemosensory disorders at disease onset: a psychophysical and magnetic resonance imaging exploratory study.

A preserved sense of smell and taste allows us to understand many environmental "messages" and results in meaningfully improvements to quality of life. With the COVID-19 pandemic, it became clear how important these senses are for social and nutritional status and catapulted this niche chemosensory research area towards widespread interest. In the current exploratory work, we assessed two groups of post-COVID-19 patients who reported having had (Group 1) or not (Group 2) a smell/taste impairment at the disease onset. The aim was to compare them using validated smell and taste tests as well as with brain magnetic resonance imaging volumetric analysis. Normative data were used for smell scores comparison and a pool of healthy subjects, recruited before the pandemic, served as controls for taste scores. The majority of patients in both groups showed an olfactory impairment, which was more severe in Group 1 (median UPSIT scores: 24.5 Group 1 vs 31.0 Group 2, p = 0.008), particularly among women (p = 0.014). No significant differences emerged comparing taste scores between Group 1 and Group 2, but dysgeusia was only present in Group 1 patients. However, for taste scores, a significant difference was found between Group 1 and controls (p = 0.005). No MRI anatomical abnormalities emerged in any patients while brain volumetric analysis suggested a significant difference among groups for the right caudate nucleus (p = 0.028), although this was not retained following Benjamini-Hochberg correction. This exploratory study could add new information in COVID-19 chemosensory long-lasting impairment and address future investigations on the post-COVID-19 patients' research.

A wearable system for visual cueing gait rehabilitation in Parkinson's disease: a randomized non-inferiority trial.

BACKGROUND: Gait disturbances represent one of the most disabling features of Parkinson's disease (PD). AIM: The aim of this study was to evaluate the non-inferiority of a new wearable visual cueing system (Q-Walk) for gait rehabilitation in PD subjects, compared to traditional visual cues (stripes on the floor). DESIGN: Open-label, monocentric, randomized controlled non-inferiority trial. SETTING: Outpatients. POPULATION: Patients affected by idiopathic PD without cognitive impairment, Hoehn and Yahr stage II-IV, Unified Parkinson's Disease Rating Scale motor section III ≥2, stable drug usage since at least 3 weeks. METHODS: At the enrollment (T0), all subjects underwent a clinical/functional evaluation and the instrumental gait and postural analysis; then they were randomly assigned to the Study Group (SG) or Control Group (CG). Rehabilitation program consisted in 10 consecutive individual sessions (5 sessions/week for 2 consecutive weeks). Each session included 60 minutes of conventional physiotherapy plus 30 minutes of gait training by Q-Walk (SG) or by traditional visual cues (CG). Follow-up visits were scheduled at the end of the treatment (T1) and after 3 months (T2). RESULTS: Fifty-two subjects were enrolled in the study, 26 in each group. The within-groups analysis showed a significant improvement in clinical scales and instrumental data at T1 and at T2, compared to baseline, in both groups. According to the between-group analysis, Q-Walk cueing system was not-inferior to the traditional cues for gait rehabilitation. The satisfaction questionnaire revealed that most subjects described the Q-Walk cueing system as simple, motivating and easily usable, possibly suitable for home use. CONCLUSIONS: Data showed that motor rehabilitation of PD subjects performed by means of the new wearable Q-Walk cueing system was feasible and as effective as traditional cues in improving gait parameters and balance. CLINICAL REHABILITATION IMPACT: Wearable devices can act as an additional rehabilitation strategy for long-term and continuous care, allowing patients to train intensively and extensively in household settings, favoring a tailor-made and personalized approach as well as remote monitoring.

Effects of upper limb vibratory stimulation training on motor symptoms in Parkinson's disease: an observational study.

OBJECTIVES: Parkinson's disease is characterized by motor and non-motor symptoms. Tremor is one of the motor symptoms that can affect manual skills and have an impact on daily activities. The aim of the current study is to investigate the effect of upper limb training provided by a specific vibratory device (Armshake®, Move It GmbH - Bochum, Germany) on tremor and motor functionality in patients with Parkinson's disease. Furthermore, the training effect on global cognitive functioning is assessed. DESIGN: An uncontrolled before-after clinical trial. PATIENTS: Individuals with diagnosis of Parkinson's disease, motor upper limbs deficits, and absence of dementia. METHODS: Participants underwent a 3-week programme (3 times a week) and was evaluated before, after, and at 1 month follow-up by motor (Fahn Tolosa Marin Tremor Rating Scale, Unified Parkinson's Disease Rating Scale - part III, Purdue Pegboard Test, Disability of the Arm, Shoulder and Hand Questionnaire) and cognitive (Montreal Cognitive Assessment) scales. RESULTS: Twenty subjects are included. After treatment a statistically significant improvement in tremor, manual dexterity and activities of daily living was found. The data indicated no effects on global cognitive functioning. CONCLUSION: These findings suggest positive effects of vibratory stimulation training on upper limb motor symptoms in Parkinson's disease.

Autoimmune Movement Disorders Complicating Treatment with Immune Checkpoint Inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICI) may trigger autoimmune neurological conditions, including movement disorders (MD). OBJECTIVES: The aim of this study was to characterize MDs occurring as immune-related adverse events (irAEs) of ICIs. METHODS: A systematic literature review of case reports/series of MDs as irAEs of ICIs was performed. RESULTS: Of 5682 eligible papers, 26 articles with 28 patients were included. MDs occur as a rare complication of cancer immunotherapy with heterogeneous clinical presentations and in most cases in association with other irAEs. Inflammatory basal ganglia T2/fluid attenuated inversion recovery abnormalities are rarely observed, but brain imaging is frequently unrevealing. Cerebrospinal fluid findings are frequently suggestive of inflammation. Half of cases are associated with a wide range of autoantibodies. Steroids and ICI withdrawal usually lead to improvement, even though some patients experienced relapses or a severe clinical course. CONCLUSION: MDs are a rare complication of ICIs that should be promptly recognized to offer patients a correct diagnosis and treatment.

Considerations for establishing and maintaining international research collaboration: the example of chemotherapy-induced peripheral neurotoxicity (CIPN)-a white paper.

PURPOSE: This white paper provides guidance regarding the process for establishing and maintaining international collaborations to conduct oncology/neurology-focused chemotherapy-induced peripheral neurotoxicity (CIPN) research. METHODS: An international multidisciplinary group of CIPN scientists, clinicians, research administrators, and legal experts have pooled their collective knowledge regarding recommendations for establishing and maintaining international collaboration to foster advancement of CIPN science. RESULTS: Experts provide recommendations in 10 categories: (1) preclinical and (2) clinical research collaboration; (3) collaborators and consortiums; (4) communication; (5) funding; (6) international regulatory standards; (7) staff training; (8) data management, quality control, and data sharing; (9) dissemination across disciplines and countries; and (10) additional recommendations about feasibility, policy, and mentorship. CONCLUSION: Recommendations to establish and maintain international CIPN research collaboration will promote the inclusion of more diverse research participants, increasing consideration of cultural and genetic factors that are essential to inform innovative precision medicine interventions and propel scientific discovery to benefit cancer survivors worldwide. RELEVANCE TO INFORM RESEARCH POLICY: Our suggested guidelines for establishing and maintaining international collaborations to conduct oncology/neurology-focused chemotherapy-induced peripheral neurotoxicity (CIPN) research set forth a challenge to multinational science, clinical, and policy leaders to (1) develop simple, streamlined research designs; (2) address logistical barriers; (3) simplify and standardize regulatory requirements across countries; (4) increase funding to support international collaboration; and (5) foster faculty mentorship.

The Association Between Neurocognitive Disorders and Gustatory Dysfunction: A Systematic Review and Meta-Analysis.

Olfactory and gustatory dysfunction have been reported in mild and major neurocognitive disorders (NCDs), with variable results. While olfactory dysfunction has been consistently explored, reports on gustatory alterations are limited. We systematically reviewed case-control studies evaluating gustatory function in NCDs with various etiologies and different neuropathology. Eighteen studies were included in the systematic review, and eight were included in the meta-analysis. Most studies were on Alzheimer's disease (AD) and Parkinson's disease (PD). Pooled analyses showed worse global taste threshold and identification (sour in particular) scores in AD than controls and worse global, sweet, and sour scores in AD compared to mild cognitive impairment (MCI). PD with MCI showed worse global, sweet, salty, and sour scores than controls and cognitively unimpaired PD. Taste dysfunction was differentially associated with the severity of cognitive deficits. Gustatory dysfunction may represent a potential cross-disease chemosensory biomarker of NCD. Whether gustatory alterations may be a pre-clinical biomarker of NCD requires further studies.

The effects of virtual reality environmental enrichments on craving to food in healthy volunteers.

RATIONALE: Environmental enrichment (EE) is a non-pharmacological approach that has been shown to be effective in reducing food-taking in rats. Studies in human volunteers are still in their infancy, given the difficulty to translate the complexity of EE in clinical practice. Virtual reality (VR) is a promising methodological approach, but no study has yet applied it to model and test EE in humans. OBJECTIVES: The present study is the first to assess the effects of virtual EE on craving for palatable food. METHODS: Eighty-one healthy volunteers (43 women) were divided into three groups: (i) exposure to a virtual EE (VR-EE), (ii) exposure to a virtual neutral environment (VR-NoEE), and (iii) without exposure to VR (No VR). Craving for palatable food at basal level and evoked by neutral and palatable food images was assessed before and after the VR simulation. Behavior during VR exposure and subjective measures related to the experience were also collected. RESULTS: VR-EE group showed a significantly greater decrease in pre-post craving difference compared to No VR for all assessments and at basal level compared to VR-NoEE. Interestingly, an inverse correlation between craving and deambulation in the VR simulation emerged in VR-EE group only. CONCLUSIONS: The study highlighted the feasibility of exposing human subjects to an EE as a virtual simulation. Virtual EE induced effects on basal craving for food that suggest the potential for further improvements of the protocol to extend its efficacy to palatable food cues.

Exploring Outcome Priorities and Real-Life Management of Chemotherapy-Induced Peripheral Neurotoxicity: A Survey of the Italian Association for the Study of Pain members.

Introduction: Chemotherapy-induced peripheral neurotoxicity (CIPN) affects nearly 70% of cancer patients after chemotherapy, causing sensory, motor, autonomic dysfunction, and neuropathic pain. The Desirability of Outcome Ranking (DOOR) framework is proposed as a better way to assess preventive or therapeutic interventions for CIPN. Methods: A survey was conducted among Italian healthcare professionals and researchers affiliated to the Italian Chapter of the International Association for the Study of Pain (AISD) to identify the most important outcomes in clinical management and research. Results: Among the 73 respondents, 61 qualified for the survey, with an overall response rate of 1.2%. The vast majority were physicians (77%), most of whom were anesthesiologists (47.5%). The results showed that pain, survival, sensory impairment, motor impairment, and quality of life were consistently ranked as the most important outcomes, but there was significant disagreement in the outcomes relative ranking, making it difficult to develop a DOOR algorithm. The study also revealed that clinicians commonly use structured interviews to evaluate patients with CIPN, and the most prescribed drugs or supplements were palmitoylethanolamide, pregabalin, gabapentin and alpha lipoic acid as preventive agents and pregabalin, palmitoylethanolamide, duloxetine, gabapentin, and amitriptyline as therapeutic agents. However, many of these drugs have not been clinically proven to be effective for CIPN. Discussion: This study suggests that the implementation of a DOOR framework for CIPN using healthcare professionals is more difficult than expected, given the significant disagreement in our respondents' ranking of outcomes. Our work provides interesting topics for future research in CIPN, but its limitations include a small sample size, a low response rate, and a possible selection bias.

Artificial intelligence for dementia prevention.

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

What's New in Neuropathy?

Neuropathic pain presents diagnostic and treatment challenges. Despite recent advances in our understanding of the diagnosis and treatment of neuropathy, much remains to be elucidated. Familiar with neuropathy is the paradox that aberrant nerve signaling causes both sensory loss and pain. Voltage-gated sodium channels play an important role in neuronal electrogenesis and communication among neurons, and their dysregulation leads to hyperexcitability and pain. While numerous validated diagnostic assessment tools are available for neuropathy, patients often experience a diagnostic delay about the cause of their neuropathy. New research is defining more specific types of neuropathy beyond peripheral and central forms. The prevalence of pain varies by type of neuropathy, with chronic idiopathic axonal polyneuropathy associated with the highest proportion of patients experiencing pain. In the majority of types, it exceeds 50%. Gluten neuropathy, a form of peripheral neuropathy, is a new diagnostic consideration. It may require electrochemical conductance testing of hands and feet to test for sudomotor dysfunction. Among those with serologically confirmed gluten sensitivity or celiac disease, gluten neuropathy is a common neurological manifestation and may be addressed at least partially by a gluten-free diet. In Greece, a new neuropathic pain registry was created in 2014 in order to help gather data from real-world neuropathic pain patients. While still in its earliest phase, this registry has already produced demographic and treatment data that suggest suboptimal prescribing and less than recommended use of interventional procedures. Awareness campaigns are underway to encourage more Greek pain clinics to participate in this important registry.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Artificial intelligence for dementia drug discovery and trials optimization.

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Artificial Intelligence for Dementia Research Methods Optimization.

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Treatment for cognitive and neuropsychiatric non-motor symptoms in Parkinson's disease: current evidence and future perspectives.

INTRODUCTION: Non-motor symptoms (NMS) affect patients with Parkinson's disease (PD) from the prodromal to the advanced stages. NMS phenotypes greatly vary and have a huge impact on patients' and caregivers' quality of life (QoL). The management of cognitive and neuropsychiatric NMS remains an unmet need. AREAS COVERED: The authors, herein, review the dopaminergic and non-dopaminergic pathogenesis, clinical features, assessment, and pharmacological and non-pharmacological treatments of cognitive and neuropsychiatric NMS in PD. They discuss the current evidence and report the findings of an overview of ongoing trials on pharmacological and selected non-pharmacological strategies. EXPERT OPINION: The treatment of cognitive and neuropsychiatric NMS in PD is poorly explored, and therapeutic options are unsatisfactory. Pharmacological treatment of cognitive NMS is based on symptomatic active principles used in Alzheimer's disease. Dopamine agonists, selective serotonin, and serotonin-norepinephrine reuptake inhibitors have some evidence on PD-related depression. Clozapine, quetiapine, and pimavanserin may be considered for psychosis in PD. Evidence on the treatment of other neuropsychiatric NMS is limited or lacking. Addressing pathophysiological and clinical issues, which hamper solid evidence on the treatment of cognitive and neuropsychiatric NMS, may reduce the impact on QoL for PD patients and their caregivers.