Feasibility of spirography features for objective assessment of motor function in Parkinson's disease

Subthalamic stimulation modulates context-dependent effects of beta bursts during fine motor control

Increasing evidence suggests a considerable role of pre-movement beta bursts for motor control and its impairment in Parkinson's disease. However, whether beta bursts occur during precise and prolonged movements and if they affect fine motor control remains unclear. To investigate the role of within-movement beta bursts for fine motor control, we here combine invasive electrophysiological recordings and clinical deep brain stimulation in the subthalamic nucleus in 19 patients with Parkinson's disease performing a context-varying task that comprised template-guided and free spiral drawing. We determined beta bursts in narrow frequency bands around patient-specific peaks and assessed burst amplitude, duration, and their immediate impact on drawing speed. We reveal that beta bursts occur during the execution of drawing movements with reduced duration and amplitude in comparison to rest. Exclusively when drawing freely, they parallel reductions in acceleration. Deep brain stimulation increases the acceleration around beta bursts in addition to a general increase in drawing velocity and improvements of clinical function. These results provide evidence for a diverse and task-specific role of subthalamic beta bursts for fine motor control in Parkinson's disease; suggesting that pathological beta bursts act in a context dependent manner, which can be targeted by clinical deep brain stimulation.

Deficient Interhemispheric Connectivity Underlies Movement Irregularities in Parkinson's Disease

BACKGROUND

Movement execution is impaired in patients with Parkinson's disease. Evolving neurodegeneration leads to altered connectivity between distinct regions of the brain and altered activity at interconnected areas. How connectivity alterations influence complex movements like drawing spirals in Parkinson's disease patients remains largely unexplored.

OBJECTIVE

We investigated whether deteriorations in interregional connectivity relate to impaired execution of drawing.

METHODS

Twenty-nine patients and 31 age-matched healthy control participants drew spirals with both hands on a digital graphics tablet, and the regularity of drawing execution was evaluated by sample entropy. We recorded resting-state fMRI and task-related EEG, and calculated the time-resolved partial directed coherence to estimate effective connectivity for both imaging modalities to determine the extent and directionality of interregional interactions.

RESULTS

Movement performance in Parkinson's disease patients was characterized by increased sample entropy, corresponding to enhanced irregularities in task execution. Effective connectivity between the motor cortices of both hemispheres, derived from resting-state fMRI, was significantly reduced in Parkinson's disease patients in comparison to controls. The connectivity strength in the nondominant to dominant hemisphere direction in both modalities was inversely correlated with irregularities during drawing, but not with the clinical state.

CONCLUSION

Our findings suggest that interhemispheric connections are affected both at rest and during drawing movements by Parkinson's disease. This provides novel evidence that disruptions of interhemispheric information exchange play a pivotal role for impairments of complex movement execution in Parkinson's disease patients.

A smart ink pen for spiral drawing analysis in patients with Parkinson's disease

Handwriting skills could be highly impaired in patients affected by Parkinson's disease (PD), and for this reason its analysis had always been considered relevant. In handwriting assessment, Archimedes spiral drawing is one of the most proposed tasks, due to its peculiar shape and ease of execution. In the last decades, digitizing tablets had been widely employed for the evaluation of the spiral performance, providing a cheap and non-invasive way to gather quantitative information, to be combined with the classical clinical examination. Despite this advantage, such approach cannot easily be adopted in an unsupervised scenario and lacks the natural feel of the traditional pen-and-paper approach. This work aims at overcoming these limitations by employing a smart ink pen, designed to write on paper and instrumented with inertial and force sensors, to automatically collect data related to spiral drawing execution of PD patients (n=30) and age-matched healthy controls (n=30). From the raw data, several time and frequency domains features were extracted and compared between the groups. The statistical analysis revealed some significant differences, showing less smooth acceleration and force profiles for PD patients. However, given the heterogeneous symptoms presented by the PD cohort, a detailed analysis of exemplifying PD patients was conducted, showing the ability of Archimedes spiral drawing to capture and quantify PD characteristic features.Clinical Relevance- Among the first clinical manifestations of the pathology, handwriting impairment appears in PD patients. It is often underestimated and not investigated properly. This easy-to-use tool could be very useful as a large-scale screening, but also for treatment efficacy evaluation and for the identification of PD subgroups.

Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States

In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems differently, it can be useful to understand what are the common issues between similar states in the European Union and how these political systems can collaborate with each other, seeking synergies, finding opportunities and saving costs. Therefore, we carried out an exploratory research among similar states of the European Union, in terms of scientific research in areas of AI technologies, namely: Portugal, Greece, Austria, Belgium and Sweden. A key finding of this research is that intelligent decision support systems (IDSS) are essential for the political decision-making process, since politics normally deals with complex and multifaceted decisions, which involve trade-offs between different stakeholders. As public health is becoming increasingly relevant in the field of the European Union, the IDSSs can provide relevant contributions, as it may allow sharing critical information and assist in the political decision-making process, especially in response to crisis situations.

A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson's disease (PD), Huntington's disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson's disease, 3 with Huntington's disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education. With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.

An Easy-to-Use and Fast Assessment of Patient-Specific DBS-Induced Changes in Hand Motor Control in Parkinson's Disease

For Parkinson's disease (PD), efficient and fast monitoring of fine motor function is fundamental for capturing transient phenomena induced by deep brain stimulation (DBS), thus, enabling a fast and accurate tuning of stimulation parameters. Tuning of DBS parameters is important for obtaining a patient-specific optimal clinical effect and to regularly compensate for disease progress. We propose a fine motor function assessment framework for capturing transient DBS-induced changes. The main goals are to obtain a fast, repeatable, objective, robust, and DBS-sensitive motor-score, in addition to a high-dimensional characterization of motor components by means of an interpretable data-driven model. To achieve this, we combine a hand motor-task, termed the copy-draw test, with a linear model for analyzing features extracted from the proposed task. The approach was tested with four patients totaling eight sessions analyzed. Our approach delivers a motor-score that is sensitive to DBS-induced changes in motor function. It can be applied repeatedly within seconds. The interpretability of the underlying machine learning model provides a direct overview of the feature relevance. This analysis allows to detect and characterize single movement components that are sensitive to DBS. The proposed assessment framework is an useful tool to push forward the data-driven identification of PD-relevant neural markers. Consequent to this end, the source code of the paradigm is made publicly available.

Parkinson's disease: Cause factors, measurable indicators, and early diagnosis

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system caused due to the loss of dopaminergic neurons. It is classified under movement disorder as patients with PD present with tremor, rigidity, postural changes, and a decrease in spontaneous movements. Comorbidities including anxiety, depression, fatigue, and sleep disorders are observed prior to the diagnosis of PD. Gene mutations, exposure to toxic substances, and aging are considered as the causative factors of PD even though its genesis is unknown. This paper reviews PD etiologies, progression, and in particular measurable indicators of PD such as neuroimaging and electrophysiology modalities. In addition to gene therapy, neuroprotective, pharmacological, and neural transplantation treatments, researchers are actively aiming at identifying biological markers of PD with the goal of early diagnosis. Neuroimaging modalities used together with advanced machine learning techniques offer a promising path for the early detection and intervention in PD patients.

Digitalized spiral drawing in Parkinson's disease: A tool for evaluating beyond the written trace

One of the current scientific challenges is to propose novel tools and tasks designed to identify new motor biomarkers in Parkinson's disease (PD). Among these, a focus has placed on drawing tasks. Independently from clinical ratings, this study aimed to evaluate the pen movement and holding in digitalized spiral drawing in individuals with PD without and with medical treatment and in healthy controls. A three-step data-driven analysis was conducted. First, the effects of spatial and temporal constraints on several variables were determined. Second, the relationship between handedness and dominance of PD symptoms was investigated for the most relevant variables. Finally, a third analysis was conducted to assess the occurrence of changes associated with PD. The first analysis revealed that the number of velocity peaks and pen altitude variations were the most relevant variables in spiral drawing for evaluating the effect of the disease and medication. The second analysis revealed that the effect of medication was present for the movement fluency only, when spirals with spatial constraints were produced at a spontaneous speed by the hand on the side of dominant PD signs. Finally, the third analysis showed that the effect of medication was greater at the beginning of drawing than at the end. Digitalized spiral drawing makes it possible to observe precisely when the kinematic changes related to the disease occur during the task. Such a simple and quick task might be of great relevance to contribute to the diagnosis and follow-up of PD.