A recent study indicates that the success of brain stimulation in enhancing motor skills is influenced more by a person’s learning ability than their age, suggesting that a tailored strategy is necessary for effective neurorehabilitation.
With age, our cognitive and motor skills decline, impacting our independence and quality of life. To combat this problem, numerous research initiatives have emerged, leading to promising technologies aimed at improvement or even complete reversal of these effects.
One area of focus is non-invasive brain stimulation, which refers to a range of techniques that can alter brain functions without surgery or implants. A noteworthy method in this category is anodal transcranial direct current stimulation (atDCS). This technique applies a steady, low electrical current through electrodes placed on the scalp to influence neuronal activity.
Despite the potential of atDCS, research has yielded mixed results, prompting inquiries into why some individuals respond positively to it while others do not. A key area of investigation has been whether age contributes to these varying responses.
Other studies have highlighted the significance of factors like baseline behavioral skills and prior training, but how these elements interact with individual behavior hasn’t been exhaustively examined, indicating a need for more precise models to predict the effects of atDCS.
Recently, scientists led by Friedhelm Hummel from EPFL uncovered a crucial factor influencing the effectiveness of atDCS: an individual’s inherent learning capabilities. The researchers investigated how these native learning abilities affect the outcomes of brain stimulation during motor task learning. Their results reveal that those with less effective learning processes gain more from stimulation, while individuals with more effective strategies may suffer adverse effects.
The research involved 40 participants: 20 middle-aged adults (ages 50-65) and 20 seniors (over 65). Each age group was split into those receiving real atDCS and those receiving a placebo.
For ten days, participants engaged in a finger-tapping exercise designed to analyze motor sequence learning while undergoing atDCS. This task required them to replicate a numerical sequence on a keypad, aiming for both speed and accuracy.
The team utilized a machine-learning model trained on publicly available data to categorize participants as either “optimal” or “suboptimal” learners based on their initial task performance. The goal was to foresee who would benefit from atDCS by evaluating their capacity to grasp task information early in the training.
The results indicated that suboptimal learners, who struggled to efficiently internalize the task initially, saw a notable boost in accuracy while performing the task with atDCS. This improvement was not restricted to older adults; suboptimal learners were also present among younger participants.
Conversely, participants employing optimal learning strategies, irrespective of age, exhibited a downward trend in performance during atDCS sessions. This difference hints that brain stimulation tends to be more advantageous for individuals facing challenges with motor tasks. Essentially, atDCS appears to have restorative properties rather than enhancing them, which carries significant implications for rehabilitation efforts.
“Using various methods in machine learning, we successfully disentangled how different factors affect individual responses to brain stimulation,” explains Pablo Maceira, the study’s primary author. “This advancement will help optimize brain stimulation effects for each person or patient.”
The findings suggest that future brain stimulation techniques will be customized to suit each individual’s specific needs, rather than relying on general characteristics like age. This personalized approach could lead to more successful interventions based on brain stimulation, focusing on specific learning mechanisms—especially crucial for neurorehabilitation, where the goal is to reacquire skills lost due to brain injuries, such as strokes or traumatic brain injuries.
“Clinicians may eventually use a more sophisticated version of our algorithm to assess whether a patient would benefit from brain stimulation therapy, thereby enhancing neurorehabilitation outcomes and personalizing treatments,” adds Hummel.
