Nico Lehmann

Learning shapes the human brain, yet structural changes underlying this process remain difficult to characterize in vivo. Recent advances in magnetic resonance imaging (MRI)—including relaxometry, magnetization transfer, proton density, and diffusion imaging—combined with improved hardware and biophysical models, now allow highly specific assessment of subtle microstructural changes during learning. Here, we review studies documenting learning-induced changes in brain microstructure. Short training intervals elicit rapid MRI-detectable changes, including increases in restricted diffusion and local tissue volume, particularly in the hippocampus, potentially reflecting early neurite and glial adaptations. Longer training periods reveal additional changes in task-relevant gray and white matter, suggestive of adaptations in myelin, neurites, and neuroglia. The link between MRI changes and behavioral improvements is inconsistent, likely due to heterogeneous temporal dynamics of plasticity and interindividual variability. Because MRI provides only indirect insight into tissue microstructure, initial studies combine complementary contrasts with multivariate statistics to reduce interpretational ambiguities. High-field imaging, cross-modal approaches such as transcranial magnetic stimulation, and cross-species studies further bridge animal models and human research. Together, these developments refine biologically grounded models of human plasticity and hold promise for translational applications in personalized learning and rehabilitation.

Motor learning induces widespread brain changes, yet the microstructural mechanisms underlying human white matter (WM) plasticity remain poorly understood. Animal studies have identified roles for neurites, glia, and myelin, but in vivo human evidence has been limited by measurement specificity. Here, we combine multi-contrast quantitative MRI (qMRI), tractometry, and a novel multivariate analysis framework to investigate the microstructural basis of WM plasticity during motor skill learning. In a longitudinal within-subject study, 24 healthy adults completed 4 weeks of balance training following a baseline control period without training. We mapped changes across tractography-defined WM pathways using complementary qMRI markers related to tissue density, myelin, neurite architecture, and iron. Multivariate analysis revealed biologically plausible, behaviorally relevant plasticity in distributed pathways—including the cortico-ponto-cerebello-thalamo-cortical loop, anterior thalamic radiation, and corticospinal tracts—with important contributions from myelin-related metrics. Notably, we observed changes consistent with training-related modulation of the aggregate g -ratio in humans. These spatially distributed effects converged into a single latent dimension predicting neocortical plasticity, suggesting a coordinated, cross-tissue mechanism of brain adaptation. This biologically interpretable framework offers a powerful new approach for investigating WM microstructure in the contexts of plasticity, development, aging, disease, and rehabilitation.

Regular physical exercise is essential for promoting healthy aging and longevity. In older adults with varying physical and cognitive decline, optimising exercise interventions is crucial to maximise benefits. A promising approach to achieve this goal is by adjusting task demands to individual abilities in turn preventing over- or underloading their abilities. In the field of motor learning, it is currently unclear whether such an optimised training improves not only performance on the trained task but also transfers to untrained motor and cognitive tasks. We conducted a randomised, single-blinded, 6-week dynamic balance training (DBT) with healthy older adults (n=30). Training was tailored to individual balance ability. Participants were assigned to either suboptimal (high or low difficulty) or optimal (moderate difficulty) training groups. Transfer effects were assessed via cognitive tasks (memory and executive) and motor tasks (untrained DBT variations and other balance tasks) measured pre-, mid- and post-intervention. Multivariate longitudinal statistical analysis showed higher performance gains in the optimal training group in three out of six sessions compared to the suboptimal groups, especially under testing conditions with high task demands. The optimal group also showed greater improvements in near motor transfer tasks mid- and post-intervention, while no significant differences were observed in the cognitive tasks. Within-group DBT learning positively correlated with transfer gains, highlighting the role of training response in achieving transfer. In conclusion, optimised task difficulty in balance training enhances both task-specific performance and related motor skills, supporting the use of personalised interventions to maintain function and independence in older adults. Highlights: Optimisation of training difficulty based on individuals abilities enables early learning gains. Moderate difficulty training may be optimal for improvements in task-specific and untrained near motor transfer tasks. Response to training is a vital predictor of transfer potential.

The human brain can show remarkable experience‐induced plasticity under conditions such as aging and pathology. However, the mapping of changes provided by many imaging approaches often lacks specificity with respect to biological tissue properties, which is relevant for treatment optimization and the evaluation of health‐promoting lifestyle factors. Training‐induced structural changes in cortical and subcortical gray matter likely reflect a mixture of various microstructural processes. In order to non‐invasively map these different microstructural contributions, we used quantitative magnetic resonance imaging (qMRI) to measure clinically‐relevant brain tissue property changes (such as iron, myelin, and water) in response to 4 weeks of motor balance training in 26 healthy young adults. Training resulted in a regionally‐specific decrease in myelin‐related magnetization transfer saturation (MT_sat) in the left frontal cortex. We also found performance‐related changes in iron‐sensitive transverse relaxation rate (R2*) in visual cortical (signal increase along with positive performance correlation) and limbic subcortical (signal decrease along with negative performance correlation) brain areas. Our study contributes to a growing body of literature investigating motor training‐induced microstructural brain plasticity. Specifically, we provide new insights into microstructural brain changes using whole‐body motor learning (balance practice) and longitudinal quantitative mapping of brain tissue properties.

The benefits of learning a motor skill extend to improved task-specific cognitive abilities. The mechanistic underpinnings of this motor-cognition relationship potentially rely on overlapping neural resources involved in both processes, an assumption lacking causal evidence. We hypothesize that interfering with prefrontal networks would inhibit concurrent motor skill performance, long-term learning and associated cognitive functions dependent on similar networks (transfer). We conducted a randomised, double-blinded, sham-controlled brain stimulation study using transcranial direct current stimulation (tDCS) in young adults spanning over three weeks to assess the role of the prefrontal regions in learning a complex balance task and long-term cognitive performance. Balance training combined with active tDCS led to higher performance variability in the trained task as compared to the sham group, impacting the process of learning a complex task without affecting the learning rate. Furthermore, active tDCS also positively influenced performance in untrained motor and cognitive tasks. The findings of this study help ascertaining the networks directly involved in learning a complex motor task and its implications on cognitive function. Hence, opening up the possibility of harnessing the observed frontal networks involved in resource mobilization in instances of aging, brain lesion/injury or dysfunction.

The capacity to learn enabled the human species to adapt to various challenging environmental conditions and pass important achievements on to the next generation. A growing body of research suggests links between neocortical folding properties and numerous aspects of human behavior, but their impact on enhanced human learning capacity remains unexplored. Here we leverage three training cohorts to demonstrate that higher levels of premotor cortical folding reliably predict individual long-term learning gains in a challenging new motor task, above and beyond initial performance differences. Individual folding-related predisposition to motor learning was found to be independent of cortical thickness and intracortical microstructure, but dependent on larger cortical surface area in premotor regions. We further show that learning-relevant features of cortical folding occurred in close spatial proximity to practice-induced structural brain plasticity. Our results suggest a link between neocortical surface folding and human behavioral adaptability.

Background: The benefits of learning a motor skill extend to improved task-specific cognitive abilities. The mechanistic underpinnings of this motor-cognition relationship potentially rely on overlapping neural resources involved in both processes, an assumption lacking causal evidence. Objectives: We hypothesize that interfering with prefrontal networks would affect concurrent motor skill performance, long-term learning and associated cognitive functions dependent on similar networks (transfer). Methods: We conducted a randomized, double-blinded, sham-controlled brain stimulation study using transcranial direct current stimulation (tDCS) in young adults spanning over three weeks to assess the role of the prefrontal regions in learning a complex balance task and long-term cognitive performance. Results: Balance training combined with active tDCS led to higher performance variability in the trained task as compared to the sham group, without affecting the learning rate. Furthermore, active tDCS also positively impacted performance in untrained motor and cognitive tasks. Conclusion: The findings of this study help ascertaining the networks directly involved in learning a complex motor task and its implications on cognitive function. Hence, opening up the possibility of harnessing the observed frontal networks involved in resource mobilization in instances of aging, brain lesion/injury or dysfunction.