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The Reciprocal Relationship Between Short- and Long-Term Motor Learning and Neurometabolites
Melina Hehl1,2,3,4, Shanti Van Malderen1,2,3, Svitlana Blashchuk3
1Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Heverlee, Belgium.
Abstract:
Skill acquisition requires practice to stimulate neuroplasticity. Changes in inhibitory and excitatory neurotransmitters, such as gamma-aminobutyric acid (GABA) and glutamate, are believed to play a crucial role in promoting neuroplasticity. Magnetic resonance spectroscopy (MRS) at 3 T, using the MEGA-PRESS sequence, and behavioral data were collected from 62 volunteers. Participants completed a 4-week protocol, practicing either complex (n = 32) or simple (n = 30) bimanual tracking tasks (BTT). Neurotransmitter levels and skill levels at baseline, after 2 and 4 weeks of motor training were compared for the left and right primary sensorimotor cortex (SM1) and the left dorsal premotor cortex (PMd). Furthermore, task-related modulations of neurotransmitter levels in the left PMd were assessed. The study yielded that baseline neurotransmitter levels in motor-related brain regions predicted training success. Furthermore, lower GABA+ (p = 0.0347) and higher Glx (glutamate + glutamine compound) levels (p = 0.0234) in left PMd correlated with better long-term learning of simple and complex tasks, respectively, whereas higher GABA+ in right SM1 correlated with complex task learning (p = 0.0064). Resting neurometabolite levels changed during the intervention: Left SM1 Glx decreased with complex training toward Week 4 (p = 0.0135), whereas right SM1 Glx was increased at Week 2 (p = 0.0043), regardless of training type. Group-level analysis showed no task-related neurometabolite modulation in the left PMd. However, individual baseline GABA+ and Glx modulation influenced short-term motor learning (interaction: p = 0.0213). These findings underscore the importance of an interplay between inhibitory and excitatory neurotransmitters during motor learning and suggest potential for future personalized approaches to optimize motor learning.
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