The Modulatory Effects of Transcranial Alternating Current Stimulation on Brain Oscillatory Patterns in the Beta Band in Healthy Older Adults.

In the last few years, transcranial alternating current stimulation (tACS) has attracted attention as a promising approach to interact with ongoing oscillatory cortical activity and, consequently, to enhance cognitive and motor processes. While tACS findings are limited by high variability in young adults' responses, its effects on brain oscillations in older adults remain largely unexplored. In fact, the modulatory effects of tACS on cortical oscillations in healthy aging participants have not yet been investigated extensively, particularly during movement.

Intersectoral health interventions to improve the well-being of people living with type 2 diabetes: a scoping review protocol.

Introduction: Intersectoral collaboration is a collaborative approach between the health sectors and other sectors to address the interdependent nature of the social determinants of health associated with chronic diseases such as diabetes. This scoping review aims to identify intersectoral health interventions implemented in primary care and community settings to improve the well-being and health of people living with type 2 diabetes.

Methods And Analysis: This protocol is developed by the Arksey and O'Malley (2005) framework for scoping reviews and the Levac methodological enhancement.

The use of enhanced intrinsic feedback for motor learning in stroke survivors: Clinical trial protocol.

Background: Stroke can lead to lasting sensorimotor deficits of the upper limb (UL) persisting into the chronic phase despite intensive rehabilitation. A major impairment of reaching after stroke is a decreased range of active elbow extension, which in turn leads to the use of compensatory movements. Retraining movement patterns relies on cognition and motor learning principles.

Deep learning prediction of motor performance in stroke individuals using neuroimaging data.

The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have been used as potential biomarkers of motor recovery, with limited accuracy up to date. To address this, the present study aimed to develop a deep learning model based on structural brain images obtained from stroke participants and healthy volunteers.