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.

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.

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.

Sex and gender differences in cognitive resilience to aging and Alzheimer's disease.

Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

Cerebellar involvement associated with immune checkpoint inhibitors: A systematic review.

BACKGROUND AND PURPOSE: Immune checkpoint inhibitors (ICIs) targeting programmed death receptor 1 (PD-1), cytotoxic T-lymphocyte-associated-4 (CTLA-4) and programmed cell death ligand 1 can be associated with immune-related adverse events (iRAEs). Amongst neurological iRAEs, cerebellar involvement seems to be rare and currently lacks a proper characterization. The aim of this study was to phenotype cerebellar iRAEs. METHODS: A systematic review was performed according to PRISMA guidelines including reported patients with cerebellar involvement related to ICIs and with available individual data. RESULTS: After screening 2765 records, 32 studies with 46 patients were included. Median age was 63 years (20-82), and most patients were male (63.0%). Isolated cerebellitis was observed in 32.6% of cases, whilst the remaining cases had "cerebellitis plus", mostly associated with encephalitis/encephalopathy. Associated tumors included most frequently lung cancer, melanoma and Merkel cell carcinoma. PD-1 inhibitor was the most administered treatment (n = 29, 64.4%), whilst exposure to CTLA-4 inhibitor was rare (n = 2, 4.5%). Magnetic resonance imaging was abnormal in 43.2% of patients and inflammatory cerebrospinal fluid findings were frequently observed. Autoantibodies were detected in 61.9% of patients and included novel reactivities. Amongst treatment strategies, the most common were steroids (n = 36) and ICI discontinuation (n = 28, 90.3%). Relapses were reported in 10% of patients. Most patients showed improvement/remission (n = 31) but, at last follow-up, 12 had died. Isolated cerebellitis versus cerebellitis-plus differed in terms of outcomes, whilst seropositive versus seronegative patients had distinct tumor associations. DISCUSSION: Cerebellar iRAEs are usually multifocal, have heterogeneous tumor associations, are most associated with PD-1 inhibitor exposure and are related to autoantibodies, including novel reactivities.

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 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 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 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.

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 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.

Spasticity following brain and spinal cord injury: assessment and treatment.

PURPOSE OF REVIEW: Spasticity is a common sequela of brain and spinal cord injury and contributes to disability, reduces quality of life, and increases economic burden. Spasticity is still incompletely recognized and undertreated. We will provide an overview of recent published data on the definition, assessment, and prediction, therapeutic advances, with a focus on promising new approaches, and telemedicine applications for spasticity. RECENT FINDINGS: Two new definitions of spasticity have been recently proposed, but operational criteria should be developed, and test-retest and inter-rater reliability should be explored. Cannabinoids proved to be effective in spasticity in multiple sclerosis, but evidence in other types of spasticity is lacking. Botulinum neurotoxin injection is the first-line therapy for focal spasticity, and recent literature focused on optimizing its efficacy. Several pharmacological, interventional, and nonpharmacological therapeutic approaches for spasticity have been explored but low-quality evidence impedes solid conclusions on their efficacy. The recent COVID-19 pandemic yielded guidelines/recommendations for the use of telemedicine in spasticity. SUMMARY: Despite the frequency of spasticity, robust diagnostic criteria and reliable assessment scales are required. High-quality studies are needed to support the efficacy of current treatments for spasticity. Future studies should explore telemedicine tools for spasticity assessment and treatment.

D2/D3 Receptor Agonism: Paving the Way for a New Therapeutic Target for Taste Disorders in Parkinson's Disease and Other Conditions?

Chemosensory (i.e., olfaction and taste) dysfunction is common in neurodegenerative (e.g., Parkinson's disease, Alzheimer's disease, and dementia), psychiatric (e.g., depression, bipolar disorders, other conditions), and postinfectious (i.e., long COVID) diseases and in the elderly. Despite its impact on patients' quality of life, no established treatment for taste disorders exists so far. A recent report on the effect of pramipexole, a D2/D3 agonist, on taste performance in healthy participants provides support for a new potential therapeutic target for taste dysfunction to be tested in future randomized, placebo-controlled, clinical trials across several populations reporting gustatory symptoms.

Wearables, sensors, and smart devices for the detection and monitoring of chemotherapy-induced peripheral neurotoxicity: Systematic review and directions for future research.

Chemotherapy-induced peripheral neurotoxicity (CIPN) diagnosis is largely based on patient reported outcomes. Wearables, sensors, and smart devices may potentially provide early detection and monitoring of CIPN. We systematically reviewed data on wearables, sensors, and smart devices to detect and/or monitor signs and symptoms of CIPN. Moreover, we provide directions and recommendations for future studies. A literature search using PubMed/MEDLINE, Web of Science, IEEE Xplore, and CINHAL databases was conducted from database inception until March 2021. The search was further updated in July 2022 to ensure currency of results. A total of 1885 records were title-abstract screened, 33 full texts were assessed, and 16 were included. The retrieved papers were heterogeneous in terms of study design, sample size, CIPN severity, chemotherapy agents, type of wearable/sensor/device applied, parameters of interest, and purpose. Data are promising and provide preliminary evidence on wearables, sensors, and smart devices for CIPN detection and monitoring. There are several issues and knowledge gaps that should be addressed. We propose a framework for future studies.

Neuromuscular complications of cancer therapy.

PURPOSE OF REVIEW: The neuromuscular complications of cancer therapy include chemotherapy-induced peripheral neurotoxicity (CIPN), immune-related neuromuscular complications to immune checkpoint inhibitors and radiation-induced neuropathy/plexopathy. With a wider focus on CIPN, we will discuss new pathogenetic insights, recent predictive biomarkers and emerging therapies for neuromuscular complications of cancer therapy. RECENT FINDINGS: Findings from recent preclinical studies have improved our knowledge on new CIPN pathogenetic pathways, including the activation of senescence-like processes in neurons, axonal degeneration and neuroinflammation. Metabolomics and serum neurofilament light chain levels appear the most promising biomarkers to predict CIPN development and severity. There is some recent evidence of promising pharmacological compounds to prevent or treat CIPN, and new drugs are in early development and testing. SUMMARY: A multimodal assessment, with neurophysiological, imaging and patient-reported outcome measures, coupled with the use of reliable blood or genetic biomarkers, may offer pathogenetic grounds for future preventive and symptomatic strategies for the multidisciplinary treatment of neuromuscular complications of cancer therapy.

Chronic fatigue syndrome: an emerging sequela in COVID-19 survivors?

SARS-CoV-2 survivors may report persistent symptoms that resemble myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). We explored (a) ME/CFS-like symptom prevalence and (b) whether axonal, inflammatory, and/or lung changes may contribute to ME/CFS-like symptoms in SARS-CoV-2 survivors through clinical, neuropsychiatric, neuropsychological, lung function assessment, and serum neurofilament light chain, an axonal damage biomarker. ME/CFS-like features were found in 27% of our sample. ME/CFS-like group showed worse sleep quality, fatigue, pain, depressive symptoms, subjective cognitive complaints, Borg baseline dyspnea of the 6-min walking test vs. those without ME/CFS-like symptoms. These preliminary findings raise concern on a possible future ME/CFS-like pandemic in SARS-CoV-2 survivors.

Adult attention-deficit/hyperactivity disorder symptoms, cognitive dysfunction and quality of life in high-dose use of benzodiazepine and Z-drug.

High-dose use of benzodiazepines (BZDs) and Z-drugs was found to be associated with adult attention deficit/hyperactivity disorder (ADHD) and multidomain cognitive deficits, but the interplay between these factors and its effect on quality of life (QoL) is unclear. We explored (a) whether cognitive dysfunction differs in high-dose BZD/Z-drug users with and without adult ADHD and (b) the impact of cognitive deficits and adult ADHD on QoL in this substance-use disorder (SUD). From January 2015 to December 2019, we recruited 207 high-dose BZD/Z-drug users seeking treatment. We assessed the presence of adult ADHD with a screening tool, which was validated in SUD patients, and collected demographic, clinical and QoL data from the 76 included patients. A neuropsychological battery explored five cognitive domains. We found that: (a) screening for adult ADHD was frequently positive; (b) Short Form-36 (SF-36), a self-administered QoL questionnaire, was worse than the general population and worse in patients positive (ADHD+) vs. those negative (ADHD-) to ADHD screening tool; (c) executive function was significantly worse in ADHD+ than ADHD- patients; (d) some SF-36 dimensions were negatively influenced by executive dysfunction; (e) multivariate analysis showed an interplay between adult ADHD and cognitive dysfunction in worsening QoL. We documented a complex interplay between adult ADHD, cognitive dysfunction and QoL in high-dose BZD/Z-drug users. Assessing adult ADHD, neuropsychological measures and QoL may offer a full scenario of these patients, who are frequently impaired in everyday activities. Future research should explore whether pharmacological treatment might improve cognitive dysfunction and QoL in this SUD.

Olfaction in patients with Parkinson's disease: a new threshold test analysis through turning points trajectories.

Olfactory deficit is a widely documented non-motor symptom in Parkinson's disease (PD). Abnormal turning points trajectories through olfactory threshold testing have been recently reported in patients with olfactory dysfunction, who seem to adapt faster to olfactory stimuli, but data on PD patients are lacking. The aim of this study is to perform olfactory threshold test and explore the turning points trajectories in PD patients in comparison to normal controls. We recruited 59 PD patients without dementia, and no conditions that could influence evaluation of olfaction and cognition. Sixty healthy subjects served as controls. Patients and controls underwent a comprehensive olfactory evaluation with the Sniffin' Sticks extended test assessing threshold, discrimination and identification and a full neuropsychological evaluation. Besides, threshold test data were analyzed examining all the turning points trajectories. PD patients showed a different olfactory threshold test pattern, i.e., faster olfactory adaptation, than controls with no effect of age. Normosmic PD patients showed different olfactory threshold test pattern, i.e., better threshold score, than normosmic controls. Visuospatial dysfunction was the only factor that significantly influenced this pattern. Olfactory threshold trajectories suggested a possible adaptation phenomenon in PD patients. Our data offered some new insights on normosmic PD patients, which appear to be a subset with a specific psychophysical profile. The analysis of the turning points trajectories, through an olfactory threshold test, could offer additional information on olfactory function in PD patients. Future larger studies should confirm these preliminary findings.

Remote clinical trials: A timely opportunity for a virtual reality approach and its potential application in neurology.

The COVID-19 pandemic boosted the expansion and development of new remote models of care and clinical research modalities. Health systems are going to implement telemedical innovations in the near future. Virtual clinical trials (VCT), also known as remote or decentralized ones, may profoundly change the way how clinical studies are conducted, for the benefit of patients with chronic and neurological diseases who are often fragile and may have limited access to traditional healthcare facilities. Despite significant progress, several limitations still need to be addressed to implement telemedicine technologies for VCT. The information and communication technology (ICT) devices (e.g., mobile apps and wearables) may be applied to VCTs but show some practical issues that may hamper the compliance with rigorous research criteria and protocols. We herewith discuss the advantages and disadvantages of virtual reality (VR) in combination with other ICT devices and solutions to improve the conduction of VCT in patients with neurological disorders. The so-called "digital divide," that is, the gap between people who can and those who cannot access high-speed and broadband internet connections, and issues related to VR, such as VR sickness, should be addressed to improve larger VCT participation to neurological patients.