Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

Motor Memory Consolidation after Augmented Variability Depends on the Space in which Variability is Introduced.

Motor memories undergo a period of consolidation before they become resistant to the practice of another task. Although movement variability is important in motor memory consolidation, its role is not fully understood in redundant tasks where variability can exist along two orthogonal subspaces (the 'task space' and the 'null space') that have different effects on task performance. Here, we used haptic perturbations to augment variability in these different spaces and examined their effect on motor memory consolidation. Participants learned a shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance travelled by the puck was determined by the sum of the left and right hand speeds at release. After participants practiced the task, we used haptic perturbations to introduce motor variability in the task space or null space and examined consolidation of the original task on the next day. We found that regardless of the amplitude, augmenting variability in the task space resulted in significantly better consolidation relative to augmenting variability in the null space, but was not different from a control group that practiced with no variability. This benefit of increasing task space variability relative to increasing null space variability was likely due to the fact that it did not disrupt the pre-existing coordination strategy. These results suggest that the effects of variability on motor memory consolidation depend on the interplay between the induced variability and the pre-existing coordination strategy.

Integrating Tactile Feedback Technologies Into Home-Based Telerehabilitation: Opportunities and Challenges in Light of COVID-19 Pandemic.

The COVID-19 pandemic has highlighted the need for advancing the development and implementation of novel means for home-based telerehabilitation in order to enable remote assessment and training for individuals with disabling conditions in need of therapy. While somatosensory input is essential for motor function, to date, most telerehabilitation therapies and technologies focus on assessing and training motor impairments, while the somatosensorial aspect is largely neglected. The integration of tactile devices into home-based rehabilitation practice has the potential to enhance the recovery of sensorimotor impairments and to promote functional gains through practice in an enriched environment with augmented tactile feedback and haptic interactions. In the current review, we outline the clinical approaches for stimulating somatosensation in home-based telerehabilitation and review the existing technologies for conveying mechanical tactile feedback (i.e., vibration, stretch, pressure, and mid-air stimulations). We focus on tactile feedback technologies that can be integrated into home-based practice due to their relatively low cost, compact size, and lightweight. The advantages and opportunities, as well as the long-term challenges and gaps with regards to implementing these technologies into home-based telerehabilitation, are discussed.