The Italian tremor Network (TITAN): rationale, design and preliminary findings

Validity of tremor analysis using smartphone compatible computer vision frameworks

Computer vision (CV)-based approaches hold promising potential for the classification and quantitative assessment of movement disorders. To take full advantage of this potential, the pipelines need to be validated against established clinical and electrophysiological gold standards. This study examines the validity of the Mediapipe (by Google) and Vision (by Apple) smartphone-enabled hand detection frameworks for tremor analysis. Both frameworks were tested in virtual experiments with simulated tremulous hands to determine the optimal camera position for hand tremor assessment and the minimum detectable tremor amplitude and frequency. Both frameworks were then compared with optical motion capture (OMC), accelerometry, and clinical ratings in 20 tremor patients. Both CV frameworks accurately measured tremor peak frequency. Significant correlations were found between CV-assessed tremor amplitudes and Essential Tremor Rating Assessment Scale (TETRAS) scores. However, the accuracy of amplitude estimation compared to OMC as ground truth was insufficient for clinical application. In conclusion, CV-based tremor analysis is an accurate and simple clinical assessment tool to determine tremor frequency. Further improvements in amplitude estimation are needed.

Subtle bradykinesia features are easier to identify and more prevalent than questionable dystonia in essential tremor

Essential tremor (ET) is characterized by upper limbs action tremor, sometimes extending to other body parts. However, ET can present with additional neurological features known as "soft signs." These signs of uncertain clinical significance are not sufficient to suggest an alternative neurological diagnosis, and include, among others, questionable dystonia and subtle voluntary movement alterations, i.e., bradykinesia and related features. This study aimed to explore the prevalence and relationship between questionable dystonia and subtle bradykinesia features in ET. Forty ET patients were video-recorded during clinical examination. Five movement disorder experts reviewed the videos to identify soft motor signs, i.e., dystonia and movement alterations during finger-tapping namely, (i) bradykinesia (reduced velocity), (ii) dysrhythmia, and (iii) sequence effect. Inter-rater agreement was quantified using the Fleiss' Kappa index. Data analysis was performed using nonparametric tests. We found a fair inter-rater agreement for upper limb dystonia (Fleiss' K = 0.27). Inter-rater agreement was higher (moderate) for head dystonia (Fleiss' K = 0.49) and finger-tapping assessment (Fleiss' K = 0.45). Upper limb dystonia was identified in 70% of patients, head dystonia in 35%, and finger-tapping alterations (in variable combinations) were observed in 95% of individuals (P < 0.001 by Fisher's exact test), including subtle bradykinesia and related features. No significant concordance or correlation was found between the soft signs. Subtle bradykinesia and related features are the most easily identifiable and frequent soft signs in ET, appearing in a higher percentage of patients than subtle dystonia. These findings provide insights into the clinical and pathophysiological understanding of ET.

Biochemical Sensors for Personalized Therapy in Parkinson's Disease: Where We Stand

Since its first introduction, levodopa has remained the cornerstone treatment for Parkinson's disease. However, as the disease advances, the therapeutic window for levodopa narrows, leading to motor complications like fluctuations and dyskinesias. Clinicians face challenges in optimizing daily therapeutic regimens, particularly in advanced stages, due to the lack of quantitative biomarkers for continuous motor monitoring. Biochemical sensing of levodopa offers a promising approach for real-time therapeutic feedback, potentially sustaining an optimal motor state throughout the day. These sensors vary in invasiveness, encompassing techniques like microdialysis, electrochemical non-enzymatic sensing, and enzymatic approaches. Electrochemical sensing, including wearable solutions that utilize reverse iontophoresis and microneedles, is notable for its potential in non-invasive or minimally invasive monitoring. Point-of-care devices and standard electrochemical cells demonstrate superior performance compared to wearable solutions; however, this comes at the cost of wearability. As a result, they are better suited for clinical use. The integration of nanomaterials such as carbon nanotubes, metal-organic frameworks, and graphene has significantly enhanced sensor sensitivity, selectivity, and detection performance. This framework paves the way for accurate, continuous monitoring of levodopa and its metabolites in biofluids such as sweat and interstitial fluid, aiding real-time motor performance assessment in Parkinson's disease. This review highlights recent advancements in biochemical sensing for levodopa and catecholamine monitoring, exploring emerging technologies and their potential role in developing closed-loop therapy for Parkinson's disease.

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review

BACKGROUND

The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance".

RESULTS

From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours.

DISCUSSION

Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.

Clinical and kinematic characterization of parkinsonian soft signs in essential tremor

BACKGROUND

Subtle parkinsonian signs, i.e., rest tremor and bradykinesia, are considered soft signs for defining essential tremor (ET) plus.

OBJECTIVES

Our study aimed to further characterize subtle parkinsonian signs in a relatively large sample of ET patients from a clinical and neurophysiological perspective.

METHODS

We employed clinical scales and kinematic techniques to assess a sample of 82 ET patients. Eighty healthy controls matched for gender and age were also included. The primary focus of our study was to conduct a comparative analysis of ET patients (without any soft signs) and ET-plus patients with rest tremor and/or bradykinesia. Additionally, we investigated the asymmetry and side concordance of these soft signs.

RESULTS

In ET-plus patients with parkinsonian soft signs (56.10% of the sample), rest tremor was clinically observed in 41.30% of cases, bradykinesia in 30.43%, and rest tremor plus bradykinesia in 28.26%. Patients with rest tremor had more severe and widespread action tremor than other patients. Furthermore, we observed a positive correlation between the amplitude of action and rest tremor. Most ET-plus patients had an asymmetry of rest tremor and bradykinesia. There was no side concordance between these soft signs, as confirmed through both clinical examination and kinematic evaluation.

CONCLUSIONS

Rest tremor and bradykinesia are frequently observed in ET and are often asymmetric but not concordant. Our findings provide a better insight into the phenomenology of ET and suggest that the parkinsonian soft signs (rest tremor and bradykinesia) in ET-plus may originate from distinct pathophysiological mechanisms.

Assessing ChatGPT Ability to Answer Frequently Asked Questions About Essential Tremor

Background

Large-language models (LLMs) driven by artificial intelligence allow people to engage in direct conversations about their health. The accuracy and readability of the answers provided by ChatGPT, the most famous LLM, about Essential Tremor (ET), one of the commonest movement disorders, have not yet been evaluated.

Methods

Answers given by ChatGPT to 10 questions about ET were evaluated by 5 professionals and 15 laypeople with a score ranging from 1 (poor) to 5 (excellent) in terms of clarity, relevance, accuracy (only for professionals), comprehensiveness, and overall value of the response. We further calculated the readability of the answers.

Results

ChatGPT answers received relatively positive evaluations, with median scores ranging between 4 and 5, by both groups and independently from the type of question. However, there was only moderate agreement between raters, especially in the group of professionals. Moreover, readability levels were poor for all examined answers.

Discussion

ChatGPT provided relatively accurate and relevant answers, with some variability as judged by the group of professionals suggesting that the degree of literacy about ET has influenced the ratings and, indirectly, that the quality of information provided in clinical practice is also variable. Moreover, the readability of the answer provided by ChatGPT was found to be poor. LLMs will likely play a significant role in the future; therefore, health-related content generated by these tools should be monitored.

Comparing Essential Tremor with and without Soft Dystonic Signs and Tremor Combined with Dystonia: The TITAN Study

BACKGROUND

Tremor disorders remain as clinical diagnoses and the rate of misdiagnosis between the commonest non-parkinsonian tremors is relatively high.

OBJECTIVES

To compare the clinical features of Essential Tremor without other features (pure ET), ET plus soft dystonic signs (ET + DS), and tremor combined with dystonia (TwD).

METHODS

We compared the clinical features of patients with pure ET, ET + DS, and TwD enrolled in The ITAlian tremor Network (TITAN). Linear regression models were performed to determine factors associated with health status and quality of life.

RESULTS

Three-hundred-eighty-three patients were included. Sex distribution was significantly different between the groups with males being more represented in pure ET and females in TwD. The initial site of tremor was different between the groups with about 40% of TwD having head tremor and ET + DS unilateral upper limb tremor at onset. This pattern mirrored the distribution of overt dystonia and soft dystonic signs at examination. Sensory trick, task-specificity, and position-dependence were more common, but not exclusive, to TwD. Pure ET patients showed the lowest degree of alcohol responsiveness and ET + DS the highest. Midline tremor was more commonly encountered and more severe in TwD than in the other groups. Regression analyses demonstrated that tremor severity, sex, age, and to a lesser degree the variable "group", independently predicted health status and quality of life, suggesting the existence of other determinants beyond tremor.

CONCLUSIONS

Pure ET and TwD manifest with a phenotypic overlap, which calls for the identification of diagnostic biomarkers. ET + DS shared features with both syndromes, suggesting intra-group heterogeneity.

Epidemiologic Disparities and Challenges in Non-parkinsonian Tremor Disorders Research: A Scoping Review Emphasizing the Indian Context

Non-parkinsonian tremors represent a heterogeneous spectrum of movement disorders where knowledge gaps persist regarding epidemiology, pathophysiology, and clinical burden. This scoping review aimed to systematically consolidate literature on these disorders in India across the domains of prevalence, biological mechanisms, psychiatric comorbidity, disability impact, and quality of life. A systematic search was undertaken across databases to identify studies on non-parkinsonian tremors in India. Extracted data were synthesized descriptively under themes spanning reported prevalence estimates and variability, proposed biological processes, psychiatric symptom rates, stigma perceptions, and quality-of-life deficits. Methodological appraisal was undertaken. Twenty-nine studies reported prevalence estimates displaying wide variability from 0.09% to 22% for essential tremor, partly attributable to definitional inconsistencies. Proposed pathologic processes centered on cerebellar dysfunction, neurotransmitter disturbances, and genetic risks. Nine studies revealed variable anxiety (6.8%-90%) and depression (3.4%-60%) rates among essential tremor patients, while two indicated perceived stigma. Five studies unanimously concurred significant quality of life impairment in essential tremors. Evidence of dystonic tremor, functional tremor, and other tremors was limited. This review exposed critical knowledge gaps and methodological limitations, while systematically evaluating the Indian literature on non-parkinsonian tremors concerning epidemiology, mechanisms, and clinical burden. Large-scale collaborative research applying standardized diagnostic criteria is imperative to determine contemporary prevalence statistics and comprehensively characterize the multifaceted disability footprint to inform patient-centric models optimizing diagnosis and holistic care.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor

Objective

To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET.

Methods

We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients.

Results

ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms.

Conclusion

This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

Clinical Management of Movement Disorders

Movement disorders include a wide and heterogeneous variety of signs and syndromes, which are classified as hyperkinetic [...].

Clinical correlates of "pure" essential tremor: the TITAN study

Background

To date, there are no large studies delineating the clinical correlates of "pure" essential tremor (ET) according to its new definition.

Methods

From the ITAlian tremor Network (TITAN) database, we extracted data from patients with a diagnosis of "pure" ET and excluded those with other tremor classifications, including ET-plus, focal, and task-specific tremor, which were formerly considered parts of the ET spectrum.

Results

Out of 653 subjects recruited in the TITAN study by January 2022, the data of 208 (31.8%) "pure" ET patients (86M/122F) were analyzed. The distribution of age at onset was found to be bimodal. The proportion of familial cases by the age-at-onset class of 20 years showed significant differences, with sporadic cases representing the large majority of the class with an age at onset above 60 years. Patients with a positive family history of tremor had a younger onset and were more likely to have leg involvement than sporadic patients despite a similar disease duration. Early-onset and late-onset cases were different in terms of tremor distribution at onset and tremor severity, likely as a function of longer disease duration, yet without differences in terms of quality of life, which suggests a relatively benign progression. Treatment patterns and outcomes revealed that up to 40% of the sample was unsatisfied with the current pharmacological options.

Discussion

The findings reported in the study provide new insights, especially with regard to a possible inversed sex distribution, and to the genetic backgrounds of "pure" ET, given that familial cases were evenly distributed across age-at-onset classes of 20 years. Deep clinical profiling of "pure" ET, for instance, according to age at onset, might increase the clinical value of this syndrome in identifying pathogenetic hypotheses and therapeutic strategies.

Early-onset inherited dystonias versus late-onset idiopathic dystonias: Same or different biological mechanisms?

Dystonia syndromes encompass a heterogeneous group of movement disorders which might be differentiated by several clinical-historical features. Among the latter, age-at-onset is probably the most important in predicting the likelihood both for the symptoms to spread from focal to generalized and for a genetic cause to be found. Accordingly, dystonia syndromes are generally stratified into early-onset and late-onset forms, the former having a greater likelihood of being monogenic disorders and the latter to be possibly multifactorial diseases, despite being currently labeled as idiopathic. Nonetheless, there are several similarities between these two groups of dystonia, including shared pathophysiological and biological mechanisms. Moreover, there is also initial evidence of age-related modifiers of early-onset dystonia syndromes and of critical periods of vulnerability of the sensorimotor network, during which a combination of genetic and non-genetic insults is more likely to produce symptoms. Based on these lines of evidence, we reappraise the double-hit hypothesis of dystonia, which would accommodate both similarities and differences between early-onset and late-onset dystonia in a single framework.

Longitudinal study of clinical and neurophysiological features in essential tremor

BACKGROUND AND PURPOSE

Essential tremor (ET) is a common and heterogeneous disorder characterized by postural/kinetic tremor of the upper limbs and other body segments and by non-motor symptoms, including cognitive and psychiatric abnormalities. Only a limited number of longitudinal studies have comprehensively and simultaneously investigated motor and non-motor symptom progression in ET. Possible soft signs that configure the ET-plus diagnosis are also under-investigated in follow-up studies. We aimed to longitudinally investigate the progression of ET manifestations by means of clinical and neurophysiological evaluation.

METHODS

Thirty-seven ET patients underwent evaluation at baseline (T0) and at follow-up (T1; mean interval ± SD = 39.89 ± 9.83 months). The assessment included the clinical and kinematic evaluation of tremor and voluntary movement execution, as well as the investigation of cognitive and psychiatric disorders.

RESULTS

A higher percentage of patients showed tremor in multiple body segments and rest tremor at T1 as compared to T0 (all p-values < 0.01). At T1, the kinematic analysis revealed reduced finger-tapping movement amplitude and velocity as compared to T0 (both p-values < 0.001). The prevalence of cognitive and psychiatric disorders did not change between T0 and T1. Female sex, absence of family history, and rest tremor at baseline were identified as predictive factors of worse disease progression.

CONCLUSIONS

ET progression is characterized by the spread of tremor in multiple body segments and by the emergence of soft signs. We also identified possible predictors of disease worsening. The results contribute to a better understanding of ET classification and pathophysiology.

May Bradykinesia Features Aid in Distinguishing Parkinson's Disease, Essential Tremor, And Healthy Elderly Individuals?

BACKGROUND

Bradykinesia is the hallmark feature of Parkinson's disease (PD); however, it can manifest in other conditions, including essential tremor (ET), and in healthy elderly individuals.

OBJECTIVE

Here we assessed whether bradykinesia features aid in distinguishing PD, ET, and healthy elderly individuals.

METHODS

We conducted simultaneous video and kinematic recordings of finger tapping in 44 PD patients, 69 ET patients, and 77 healthy elderly individuals. Videos were evaluated blindly by expert neurologists. Kinematic recordings were blindly analyzed. We calculated the inter-raters agreement and compared data among groups. Density plots assessed the overlapping in the distribution of kinematic data. Regression analyses and receiver operating characteristic curves determined how the kinematics influenced the likelihood of belonging to a clinical score category and diagnostic group.

RESULTS

The inter-rater agreement was fair (Fleiss K = 0.32). Rater found the highest clinical scores in PD, and higher scores in ET than healthy elderly individuals (p < 0.001). In regard to kinematic analysis, the groups showed variations in movement velocity, with PD presenting the slowest values and ET displaying less velocity than healthy elderly individuals (all ps < 0.001). Additionally, PD patients showed irregular rhythm and sequence effect. However, kinematic data significantly overlapped. Regression analyses showed that kinematic analysis had high specificity in differentiating between PD and healthy elderly individuals. Nonetheless, accuracy decreased when evaluating subjects with intermediate kinematic values, i.e., ET patients.

CONCLUSION

Despite a considerable degree of overlap, bradykinesia features vary to some extent in PD, ET, and healthy elderly individuals. Our findings have implications for defining bradykinesia and categorizing patients.

Age and Sex Affect Essential Tremor (ET) Plus: Clinical Heterogeneity in ET Based on the National Survey in China

The new term essential tremor (ET) plus was proposed in the 2018 tremor consensus criteria. The National Survey of Essential Tremor Plus in China, a large multicenter registry study, aimed to evaluate the clinical features of pure ET and ET plus and explore possible factors related to ET plus. All patients with ET underwent neurological examination and neuropsychological assessment at 17 clinical sites. The diagnosis was made according to the 2018 consensus criteria. Clinicodemographic characteristics were analyzed. A total of 1160 patients were included, including 546 patients with pure ET and 614 patients with ET plus. The proportion of females was significantly higher in the ET plus than that in the pure ET (P = 0.001). The age at onset (AAO) of pure ET showed a bimodal distribution, with peaks in the 2nd and 5th decades. However, the AAO of the ET plus group demonstrated a skewed distribution, with a single peak in the 6th decade. Female sex (OR=1.645, P<0.001), older age (OR=1.023, P<0.001), lower educational level (OR=0.934, P<0.001), head tremor (OR=1.457, P<0.001), and higher the Tremor Research Group Essential Tremor Rating Assessment Scale (TETRAS)-II scores (OR=1.134, P<0.001) were significantly associated with ET plus. Old age and female sex may contribute to ET plus development. Pure ET showed a bimodal distribution for AAO, whereas ET plus showed a unimodal distribution. It remains unclear whether pure ET and ET plus are merely different stages of a single disease or represent distinct disease entities.