Oculomotor Dysfunction in Idiopathic and LRRK2-Parkinson's Disease and At-Risk Individuals.

Background: Video-oculography constitutes a highly-sensitive method of characterizing ocular movements, which could detect subtle premotor changes and contribute to the early diagnosis of Parkinson's disease (PD). Objective: To investigate potential oculomotor differences between idiopathic PD (iPD) and PD associated with the G2019S variant of LRRK2 (L2PD), as well as to evaluate oculomotor function in asymptomatic carriers of the G2019S variant of LRRK2. Methods: The study enrolled 129 subjects: 30 PD (16 iPD, 14 L2PD), 23 asymptomatic carriers, 13 non-carrier relatives of L2PD patients, and 63 unrelated HCs. The video-oculographic evaluation included fixation, prosaccade, antisaccade, and memory saccade tests. Results: We did not find significant differences between iPD and L2PD. Compared to controls, PD patients displayed widespread oculomotor deficits including larger microsaccades, hypometric vertical prosaccades, increased latencies in all tests, and lower percentages of successful antisaccades and memory saccades. Non-carrier relatives showed oculomotor changes with parkinsonian features, such as fixation instability and hypometric vertical saccades. Asymptomatic carriers shared multiple similarities with PD, including signs of unstable fixation and hypometric vertical prosaccades; however, they were able to reach percentages of successful antisaccade and memory saccades similar to controls, although at the expense of longer latencies. Classification accuracy of significant oculomotor parameters to differentiate asymptomatic carriers from HCs ranged from 0.68 to 0.74, with BCEA, a marker of global fixation instability, being the parameter with the greatest classification accuracy. Conclusions: iPD and LRRK2-G2019S PD patients do not seem to display a differential oculomotor profile. Several oculomotor changes in asymptomatic carriers of LRRK2 mutations could be considered premotor biomarkers.

Innovative Metaheuristic Optimization Approach with a Bi-Triad for Rehabilitation Exoskeletons.

The present work proposes a comprehensive metaheuristic methodology for the development of a medical robot for the upper limb rehabilitation, which includes the topological optimization of the device, kinematic models (5 DOF), human-robot interface, control and experimental tests. This methodology applies two cutting-edge triads: (1) the three points of view in engineering design (client, designer and community) and (2) the triad formed by three pillars of Industry 4.0 (autonomous machines and systems, additive manufacturing and simulation of virtual environments). By applying the proposed procedure, a robotic mechanism was obtained with a reduction of more than 40% of its initial weight and a human-robot interface with three modes of operation and a biomechanically viable kinematic model for humans. The digital twin instance and its evaluation through therapeutic routines with and without disturbances was assessed; the average RMSEs obtained were 0.08 rad and 0.11 rad, respectively. The proposed methodology is applicable to any medical robot, providing a versatile and effective solution for optimizing the design and development of healthcare devices. It adopts an innovative and scalable approach to enhance their processes.

Exploring Cognitive Dysfunction in Long COVID Patients: Eye Movement Abnormalities and Frontal-Subcortical Circuits Implications via Eye-Tracking and Machine Learning.

BACKGROUND: Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by several brain areas, such as the dorsolateral prefrontal cortex and frontal-thalamic circuits, provide a potential metric for assessing cortical networks and cognitive status. We aimed to examine the utility of eye movement measurements in identifying cognitive impairments in long COVID patients. METHODS: We recruited 40 long COVID patients experiencing subjective cognitive complaints and 40 healthy controls and used a certified eye-tracking medical device to record saccades and antisaccades. Machine learning was applied to enhance the analysis of eye movement data. RESULTS: Patients did not differ from the healthy controls regarding age, sex, and years of education. However, the patients' Montreal Cognitive Assessment total score was significantly lower than healthy controls. Most eye movement parameters were significantly worse in patients. These included the latencies, gain (computed as the ratio between stimulus amplitude and gaze amplitude), velocities, and accuracy (evaluated by the presence of hypermetric or hypometria dysmetria) of both visually and memory-guided saccades; the number of correct memory saccades; the latencies and duration of reflexive saccades; and the number of errors in the antisaccade test. Machine learning permitted distinguishing between long COVID patients experiencing subjective cognitive complaints and healthy controls. CONCLUSION: Our findings suggest impairments in frontal subcortical circuits among long COVID patients who report subjective cognitive complaints. Eye-tracking, combined with machine learning, offers a novel, efficient way to assess and monitor long COVID patients' cognitive dysfunctions, suggesting its utility in clinical settings for early detection and personalized treatment strategies. Further research is needed to determine the long-term implications of these findings and the reversibility of cognitive dysfunctions.

Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases.

Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject's available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in n=17 healthy volunteers (9 males, 8 females, age 28.66±8.8 years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease.