Recalibrating the Why and Whom of Animal Models in Parkinson Disease: A Clinician's Perspective.

Animal models have been used to gain pathophysiologic insights into Parkinson's disease (PD) and aid in the translational efforts of interventions with therapeutic potential in human clinical trials. However, no disease-modifying therapy for PD has successfully emerged from model predictions. These translational disappointments warrant a reappraisal of the types of preclinical questions asked of animal models. Besides the limitations of experimental designs, the one-size convergence and oversimplification yielded by a model cannot recapitulate the molecular diversity within and between PD patients. Here, we compare the strengths and pitfalls of different models, review the discrepancies between animal and human data on similar pathologic and molecular mechanisms, assess the potential of organoids as novel modeling tools, and evaluate the types of questions for which models can guide and misguide. We propose that animal models may be of greatest utility in the evaluation of molecular mechanisms, neural pathways, drug toxicity, and safety but can be unreliable or misleading when used to generate pathophysiologic hypotheses or predict therapeutic efficacy for compounds with potential neuroprotective effects in humans. To enhance the translational disease-modification potential, the modeling must reflect the biology not of a diseased population but of subtypes of diseased humans to distinguish What data are relevant and to Whom.

SOD1-Related Cerebellar Ataxia and Motor Neuron Disease: Cp Variant as Functional Modifier?

We describe a novel superoxide dismutase (SOD1) mutation-associated clinical phenotype of cerebellar ataxia and motor neuron disease with a variant in the ceruloplasmin (Cp) gene, which may have possibly contributed to a multi-factorial phenotype, supported by genetic and protein structure analyses.

Special Issue: Immune-Mediated Neurological Disorders.

For a long time, the immune system has been considered responsible for only a minority of neurological conditions involving the central and peripheral nervous system (CNS, PNS), respectively, namely multiple sclerosis and myasthenia gravis (with myastheniform syndromes) [...].

Embracing the Science of Motherhood: Pregnancy's Transformative Effects on the Central Nervous System and the Radiance of Maternal Hormones and Immune Responses.

Pregnancy is often thought of as a time of happiness and anticipation, however, for some women, it can bring about significant emotional distress and feelings of vulnerability. The physiological changes that occur during pregnancy, including hormonal fluctuations and alterations to the immune and physical systems, can affect various parts of the body, including the central nervous system (CNS). As a result, existing conditions may be intensified or new ones, such as neurologic or psychiatric disorders, may arise, exposing women to increased risk of life-threatening conditions or suicide, in the worst-case scenarios. Given the impact of pregnancy on CNS diseases, it is crucial for healthcare providers and patients alike to be aware of these potential effects. By understanding how pregnancy may affect the CNS, clinicians can take appropriate steps to ensure that women receive the care and support they need to minimize any negative outcomes for both the mother and the baby. This paper aims to review the available evidence on the impact of pregnancy on CNS diseases, including mental health conditions, from both the clinical and biomolecular perspectives. By illuminating this crucial subject, this study fosters a delicate understanding within both patients and healthcare providers, thereby paving the way for enhanced outcomes for women throughout their pregnancy journey and beyond.

Acoustic analysis in stuttering: a machine-learning study.

Background: Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS). Objective: We assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine - SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering. Methods: Fifty-three PWS (20 children, 33 younger adults) and 71 age-/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN). Results: Acoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings. Conclusion: Acoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment).

Parkinson's disease diagnosis codes are insufficiently accurate for electronic health record research and differ by race.

BACKGROUND: There are no evidence-based guidelines for data cleaning of electronic health record (EHR) databases in Parkinson's disease (PD). Previous filtering criteria have primarily used the 9th International Statistical Classification of Diseases and Related Health Problems (ICD) with variable accuracy for true PD cases. Prior studies have not excluded atypical or drug-induced parkinsonism, and little is known about differences in accuracy by race. OBJECTIVE: To determine if excluding parkinsonism diagnoses improves accuracy of ICD-9 and -10 PD diagnosis codes. METHODS: We included ≥2 instances of an ICD-9 and/or -10 code for PD. We removed any records with at least one code indicating atypical or drug-induced parkinsonism first in all races, and then in Non-Hispanic White and Black patients. We manually reviewed 100 randomly selected charts per group before and after filtering, and performed a test of proportion (null hypothesis 0.5) for confirmed PD. RESULTS: 5633 records had ≥2 instances of a PD code. 2833 remained after filtering. The rate of true PD cases was low before and after filtering to remove parkinsonism codes (0.55 vs. 0.51, p = 0.84). Accuracy was lowest in Black patients before filtering (0.48, p = 0.69), but filtering had a greater (though modest) impact on accuracy (0.68, p < 0.001). CONCLUSIONS: There was inadequate accuracy of PD diagnosis codes in the largest study of ICD-9 and -10 codes. Accuracy was lowest in Black patients but improved the most with removing other parkinsonism codes. This highlights the limitations of using current real-world EHR data in PD research and need for further study.

Extreme phenotypic heterogeneity in non-expansion spinocerebellar ataxias.

Although the best-known spinocerebellar ataxias (SCAs) are triplet repeat diseases, many SCAs are not caused by repeat expansions. The rarity of individual non-expansion SCAs, however, has made it difficult to discern genotype-phenotype correlations. We therefore screened individuals who had been found to bear variants in a non-expansion SCA-associated gene through genetic testing, and after we eliminated genetic groups that had fewer than 30 subjects, there were 756 subjects bearing single-nucleotide variants or deletions in one of seven genes: CACNA1A (239 subjects), PRKCG (175), AFG3L2 (101), ITPR1 (91), STUB1 (77), SPTBN2 (39), or KCNC3 (34). We compared age at onset, disease features, and progression by gene and variant. There were no features that reliably distinguished one of these SCAs from another, and several genes-CACNA1A, ITPR1, SPTBN2, and KCNC3-were associated with both adult-onset and infantile-onset forms of disease, which also differed in presentation. Nevertheless, progression was overall very slow, and STUB1-associated disease was the fastest. Several variants in CACNA1A showed particularly wide ranges in age at onset: one variant produced anything from infantile developmental delay to ataxia onset at 64 years of age within the same family. For CACNA1A, ITPR1, and SPTBN2, the type of variant and charge change on the protein greatly affected the phenotype, defying pathogenicity prediction algorithms. Even with next-generation sequencing, accurate diagnosis requires dialogue between the clinician and the geneticist.

Dysfunctional Networks in Functional Dystonia.

Functional dystonia, the second most common functional movement disorder, is characterized by acute or subacute onset of fixed limb, truncal, or facial posturing, incongruent with the action-induced, position-sensitive, and task-specific manifestations of dystonia. We review neurophysiological and neuroimaging data as the basis for a dysfunctional networks in functional dystonia. Reduced intracortical and spinal inhibition contributes to abnormal muscle activation, which may be perpetuated by abnormal sensorimotor processing, impaired selection of movements, and hypoactive sense of agency in the setting of normal movement preparation but abnormal connectivity between the limbic and motor networks. Phenotypic variability may be related to as-yet undefined interactions between abnormal top-down motor regulation and overactivation of areas implicated in self-awareness, self-monitoring, and active motor inhibition such as the cingulate and insular cortices. While there remain many gaps in knowledge, further combined neurophysiological and neuroimaging assessments stand to inform the neurobiological subtypes of functional dystonia and the potential therapeutic applications.

Paraneoplastic Neurological Syndromes of the Central Nervous System: Pathophysiology, Diagnosis, and Treatment.

Paraneoplastic neurological syndromes (PNS) include any symptomatic and non-metastatic neurological manifestations associated with a neoplasm. PNS associated with antibodies against intracellular antigens, known as "high-risk" antibodies, show frequent association with underlying cancer. PNS associated with antibodies against neural surface antigens, known as "intermediate- or low-risk" antibodies, are less frequently associated with cancer. In this narrative review, we will focus on PNS of the central nervous system (CNS). Clinicians should have a high index of suspicion with acute/subacute encephalopathies to achieve a prompt diagnosis and treatment. PNS of the CNS exhibit a range of overlapping "high-risk" clinical syndromes, including but not limited to latent and overt rapidly progressive cerebellar syndrome, opsoclonus-myoclonus-ataxia syndrome, paraneoplastic (and limbic) encephalitis/encephalomyelitis, and stiff-person spectrum disorders. Some of these phenotypes may also arise from recent anti-cancer treatments, namely immune-checkpoint inhibitors and CAR T-cell therapies, as a consequence of boosting of the immune system against cancer cells. Here, we highlight the clinical features of PNS of the CNS, their associated tumors and antibodies, and the diagnostic and therapeutic strategies. The potential and the advance of this review consists on a broad description on how the field of PNS of the CNS is constantly expanding with newly discovered antibodies and syndromes. Standardized diagnostic criteria and disease biomarkers are fundamental to quickly recognize PNS to allow prompt treatment initiation, thus improving the long-term outcome of these conditions.

Ménière's Disease: Insights from an Italian Nationwide Survey.

The aim of the present study was to obtain data from a large community sample of patients with Ménière's disease (MD) in Italy through a web-based nationwide survey. Demographic, clinical, and epidemiological features of MD among members of the Italian Association of Ménière's Disease (AMMI) were collected through a web-based survey. The questionnaire was posted on the AMMI website between 01/SEP/2021 and 31/OCT/2021. A total of 520 patients (374 F, 146 M) with MD were included. The age at interview (average ± standard deviation, SD) was 51.4 ± 10.9 years, with a disease duration of 9.9 ± 9.8 years. Eighty percent of cases were unilateral. No patients reported neurocognitive disorders or Parkinson's disease. A positive family history of MD was reported in 13% of participants, while a history of allergic diseases was reported in 33%. Comorbid thyroid disorders were present in 25% of participants, and 28% used betahistine as the main treatment. To our knowledge, this is the first study that has investigated the epidemiology and current patterns of care of MD in Italy, using an anonymous survey directly sent to patients, thus implying their active participation. We hope that future studies will support the utilization of web-based surveys to address the unmet needs in the management of patients with MD.

Diagnostic Uncertainties in Tremor.

The approach and diagnosis of patients with tremor may be challenging for clinicians. According to the most recent consensus statement by the Task Force on Tremor of the International Parkinson Movement Disorder Society, the differentiation between action (i.e., kinetic, postural, intention), resting, and other task- and position-specific tremors is crucial to this goal. In addition, patients with tremor must be carefully examined for other relevant features, including the topography of the tremor, since it can involve different body areas and possibly associate with neurological signs of uncertain significance. Following the characterization of major clinical features, it may be useful to define, whenever possible, a particular tremor syndrome and to narrow down the spectrum of possible etiologies. First, it is important to distinguish between physiological and pathological tremor, and, in the latter case, to differentiate between the underlying pathological conditions. A correct approach to tremor is particularly relevant for appropriate referral, counseling, prognosis definition, and therapeutic management of patients. The purpose of this review is to outline the possible diagnostic uncertainties that may be encountered in clinical practice in the approach to patients with tremor. In addition to an emphasis on a clinical approach, this review discusses the important ancillary role of neurophysiology and innovative technologies, neuroimaging, and genetics in the diagnostic process.

Stem Cell Therapies in Movement Disorders: Lessons from Clinical Trials.

Stem cell-based therapies (SCT) to treat neurodegenerative disorders have promise but clinical trials have only recently begun, and results are not expected for several years. While most SCTs largely lead to a symptomatic therapeutic effect by replacing lost cell types, there may also be disease-modifying therapeutic effects. In fact, SCT may complement a multi-drug, subtype-specific therapeutic approach, consistent with the idea of precision medicine, which matches molecular therapies to biological subtypes of disease. In this narrative review, we examine published and ongoing trials in SCT in Parkinson's Disease, atypical parkinsonian disorders, Huntington's disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia in humans. We discuss the benefits and pitfalls of using this treatment approach within the spectrum of disease-modification efforts in neurodegenerative diseases. SCT may hold greater promise in the treatment of neurodegenerative disorders, but much research is required to determine the feasibility, safety, and efficacy of these complementary aims of therapeutic efforts.

Functional Movement Disorders and Deep Brain Stimulation: A Multi-Center Study.

Background: Functional movement disorders (FMD) are a commonly under-recognized diagnosis in patients with underlying neurodegenerative diseases. FMD have been observed in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD) and other movement disorders. The prevalence of coexisting FMD among movement disorder-related DBS patients is unknown, and it may occur more often than previously recognized. Methods: We retrospectively assessed the relative prevalence and clinical characteristics of FMD occurring post-DBS, in PD and dystonia patients (FMD+, n = 29). We compared this cohort with age at surgery-, sex-, and diagnosis-matched subjects without FMD post-DBS (FMD-, n = 29). Results: Both the FMD prevalence (0.2%-2.1%) and the number of cases/DBS procedures/year varied across centers (0.15-3.65). A total of nine of 29 FMD+ cases reported worse outcomes following DBS. Although FMD+ and FMD- manifested similar features, FMD+ showed higher psychiatric comorbidity. Conclusions: DBS may be complicated by the development of FMD in a subset of patients, particularly those with pre-morbid psychiatric conditions.

Bradykinesia in Neurodegenerative Disorders: A Blinded Video Analysis of Pathology-Proven Cases.

BACKGROUND: Bradykinesia is a cardinal feature in parkinsonisms. No study has assessed the differential features of bradykinesia in patients with pathology-proven synucleinopathies and tauopathies. OBJECTIVE: We examined whether bradykinesia features (speed, amplitude, rhythm, and sequence effect) may differ between pathology-proven synucleinopathies and tauopathies. METHODS: Forty-two cases who underwent autopsy were included and divided into synucleinopathies (Parkinson's disease and dementia with Lewy bodies) and tauopathies (progressive supranuclear palsy). Two raters blinded to the diagnosis retrospectively scored the Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III and Modified Bradykinesia Rating Scale on standardized videotaped neurological examinations. Bradykinesia scores were compared using the Mann-Whitney test and logistic regression models to adjust for disease duration. RESULTS: Demographic and clinical parameters were similar between synucleinopathies and tauopathies. There were no differences between speed, amplitude, rhythm, and sequence effect in synucleinopathies and tauopathies in unadjusted comparisons and adjusted models (all P > 0.05). CONCLUSIONS: Clinical bradykinesia features do not distinguish the underlying neuropathology in neurodegenerative parkinsonisms. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Does Head Tremor Predict Postural Instability After Bilateral Thalamic Stimulation in Essential Tremor?

Essential tremor (ET) may present with head tremor (HT), of presumed cerebellar nature. Deep brain stimulation (DBS) targeting the ventral intermediate (Vim) nucleus of the thalamus is a highly effective therapy for medication-refractory ET. However, stimulation-related side effects may include cerebellar abnormalities, such as postural instability. This retrospective cohort study evaluated the risk of post-Vim DBS postural instability (primary outcome measure) in patients with versus without head tremor (HT vs. nHT). The primary outcome measure, namely post-DBS postural instability, was assessed in both groups using a Wilcoxon rank sum t-test. The time to postural instability was determined using Cox proportional hazards regression analysis adjusted for age and sex. Out of 30 patients analyzed during the follow up period, there was similar postural instability detected in HT (9/14, 64%) and nHT patients (11/16, 69%) at 24 months post-Vim DBS (p=0.82), adjusted hazard ratio[aHR]=0.82, p=0.69). These data suggest that the presence or absence of HT does not have an impact on postural instability after bilateral Vim DBS in patients with ET.

Targeting the Complement-Sphingolipid System in COVID-19 and Gaucher Diseases: Evidence for a New Treatment Strategy.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2)-induced disease (COVID-19) and Gaucher disease (GD) exhibit upregulation of complement 5a (C5a) and its C5aR1 receptor, and excess synthesis of glycosphingolipids that lead to increased infiltration and activation of innate and adaptive immune cells, resulting in massive generation of pro-inflammatory cytokines, chemokines and growth factors. This C5a-C5aR1-glycosphingolipid pathway- induced pro-inflammatory environment causes the tissue damage in COVID-19 and GD. Strikingly, pharmaceutically targeting the C5a-C5aR1 axis or the glycosphingolipid synthesis pathway led to a reduction in glycosphingolipid synthesis and innate and adaptive immune inflammation, and protection from the tissue destruction in both COVID-19 and GD. These results reveal a common involvement of the complement and glycosphingolipid systems driving immune inflammation and tissue damage in COVID-19 and GD, respectively. It is therefore expected that combined targeting of the complement and sphingolipid pathways could ameliorate the tissue destruction, organ failure, and death in patients at high-risk of developing severe cases of COVID-19.

Are smartphones and machine learning enough to diagnose tremor?

BACKGROUND: Patients with essential tremor (ET), Parkinson's disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics. OBJECTIVES: To use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natural clusterings of patients correspond to assigned clinical diagnoses. METHODS: A supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of 27 patients were also studied. Next, we focused on a subset of 48 patients with both smartphone-based tremor measurements and detailed clinical assessment metrics and compared two separate machine learning classifiers trained on these data. RESULTS: The classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity of 84% in classifying PD from ET or DT. The algorithm classified ET from non-ET with 88% accuracy, but only classified DT from non-DT with 29% accuracy. A poorer performance was found in the independent cohort. Classifiers trained on accelerometer and clinical data respectively obtained similar results. CONCLUSIONS: Machine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremors due to lack of specificity in clinical and neuropathological features, reinforcing that they may represent overlapping syndromes.

Handwriting Declines With Human Aging: A Machine Learning Study.

Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and Methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.

Droxidopa reduces postural sway in Parkinson disease patients with orthostatic hypotension.

We evaluate the effect of droxidopa on gait and balance measures in nine patients with Parkinson's disease and neurogenic orthostatic hypotension. Computerized gait/balance analysis showed a significant effect of droxidopa in reducing postural sway. Future studies may determine if such effect translates into improvement in postural reflexes and falls.