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Bernstein-Eliav, M.

Publications and source records attributed to Bernstein-Eliav, M..

5 recordsLinked to original sources

Musical training-induced plasticity reflects melodic rather than instrumental expertise

Musical training produces behavioral gains that extend beyond the practiced context. Yet, it is unclear whether such generalization is accompanied by broadly transferable neural changes or whether training-induced plasticity remains tied to the specific features of the learned material. In this study, 80 musically naive adults completed four piano-training sessions and underwent functional MRI before and after training. Twenty untrained participants served as passive controls. Participants performed a passive listening task in which they listened to the trained melody (Fur Elise) and an untrained melody (Ode to Joy), each played on a trained musical instrument (piano) and an untrained musical instrument (saxophone). Following training, participants demonstrated improved accuracy and rhythmic performance when playing an unfamiliar melody, indicating behavioral generalization. Training-induced neural changes, however, were predominantly associated with the learned melodic content. Listening to Fur Elise elicited increased activity in the intraparietal and supplementary motor cortex and in the cerebellum, as well as decreased activity in the superior temporal cortex. Consistently, pre- and post-training scans were most accurately classified based on brain activity to Fur Elise played on piano or saxophone, rather than brain response to Ode to Joy, suggesting that neural reorganization was driven more strongly by the practiced melody than by the trained instrumental timbre. Training also enhanced the neural separability of both melodic and instrumental information. Critically, pre-training responses to Fur Elise played on the piano predicted subsequent playing accuracy, whereas pre-training sensitivity to instrumental timbre predicted rhythmic performance in a novel melodic context. Together, these findings suggest that while acquired musical skills behaviorally transfer to novel material, practice-induced functional plasticity remains predominantly melody-specific. We conclude that musical skill acquisition reflects complementary contributions of pre-existing neural characteristics and learning-induced plasticity, with baseline auditory representations potentially supporting successful behavioral transfer.

neuroscience↗

Continuous structural neuroplasticity during motor learning - a diffusion MRI study

How does our brain transform when we encounter a new task? To fully answer this question, comparing brain states before and after learning may not be enough, but rather an on-going, continuous monitoring of brain changes during learning is required. While such continuous examinations of functional learning-induced changes are widely available using functional magnetic resonance imaging (fMRI), a continuous investigation of microstructural brain modifications during learning is yet to be reported. Here, we continuously acquire diffusion MRI images during task performance. We then compute the mean diffusivity (MD) using a sliding-window approach, resulting in a continuous measure of microstructural changes throughout learning. We demonstrate the utility of this method on a motor sequence learning (finger tapping) task (n=58). MD decrease was detected in task-related brain regions, including the parahippocampal gyrus, hippocampus, inferior temporal gyrus, and cerebellum. Analysis of the temporal patterns of decrease revealed a rapid MD reduction in the right temporal gyrus after 11 minutes of learning, with additional decrease in the right parahippocampal gyrus and left cerebellum after 22 minutes. We further computed "neuroplasticity networks" of brain areas showing similar change patterns and detected similarities between these networks and canonical functional connectivity networks. Our findings offer novel insights on the spatio-temporal dynamics of microstructural neuroplasticity by demonstrating continuous modifications during the encoding phase of learning itself, rather than comparing pre- and post-learning states.

neuroscience↗

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

The search for an ideal approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

neuroscience↗

Predicting Individual Traits from Unperformed Tasks

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

neuroscience↗

Short-Term Plasticity Following Motor Sequence Learning Revealed by Diffusion MRI

Current non-invasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion MRI was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work we used diffusion MRI to investigate the short-term neuroplasticity that accompanies sequential motor learning. Following a 45-minutes training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. A second learning session taking place immediately afterwards shifted the attention of participants onto timing of key pressing instead of accuracy. This second session induced a different plasticity pattern, demonstrating the dynamic nature of this phenomenon, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.

neuroscience↗