Dataset Information

Baseline sensorimotor GABA levels shape neuroplastic processes induced by motor learning in older adults.

ABSTRACT: Previous research in young adults has demonstrated that both motor learning and transcranial direct current stimulation (tDCS) trigger decreases in the levels of gamma-aminobutyric acid (GABA) in the sensorimotor cortex, and these decreases are linked to greater learning. Less is known about the role of GABA in motor learning in healthy older adults, a knowledge gap that is surprising given the established aging-related reductions in sensorimotor GABA. Here, we examined the effects of motor learning and subsequent tDCS on sensorimotor GABA levels and resting-state functional connectivity in the brains of healthy older participants. Thirty-six older men and women completed a motor sequence learning task before receiving anodal or sham tDCS to the sensorimotor cortex. GABA-edited magnetic resonance spectroscopy of the sensorimotor cortex and resting-state (RS) functional magnetic resonance imaging data were acquired before and after learning/stimulation. At the group level, neither learning nor anodal tDCS significantly modulated GABA levels or RS connectivity among task-relevant regions. However, changes in GABA levels from the baseline to post-learning session were significantly related to motor learning magnitude, age, and baseline GABA. Moreover, the change in functional connectivity between task-relevant regions, including bilateral motor cortices, was correlated with baseline GABA levels. These data collectively indicate that motor learning-related decreases in sensorimotor GABA levels and increases in functional connectivity are limited to those older adults with higher baseline GABA levels and who learn the most. Post-learning tDCS exerted no influence on GABA levels, functional connectivity or the relationships among these variables in older adults.

SUBMITTER: King BR

PROVIDER: S-EPMC7416055 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Baseline sensorimotor GABA levels shape neuroplastic processes induced by motor learning in older adults.

King Bradley R BR Rumpf Jost-Julian JJ Verbaanderd Elvire E Heise Kirstin F KF Dolfen Nina N Sunaert Stefan S Doyon Julien J Classen Joseph J Mantini Dante D Puts Nicolaas A J NAJ Edden Richard A E RAE Albouy Geneviève G Swinnen Stephan P SP

Human brain mapping 20200625 13

Previous research in young adults has demonstrated that both motor learning and transcranial direct current stimulation (tDCS) trigger decreases in the levels of gamma-aminobutyric acid (GABA) in the sensorimotor cortex, and these decreases are linked to greater learning. Less is known about the role of GABA in motor learning in healthy older adults, a knowledge gap that is surprising given the established aging-related reductions in sensorimotor GABA. Here, we examined the effects of motor lear ...[more]

PMID: 32583940

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Project description:Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error. However, it is unclear how this optimization process is implemented in the brain, presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command, and largely ignored the underlying trial-by-trial learning process. In contrast, recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting (i.e., memory decay), which is usually considered as an inconvenient factor in motor learning, plays an important role in minimizing the motor effort cost. Here, we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex (M1) neurons. First, using a simple linear neural network model, we theoretically demonstrated that: 1) this algorithm consistently leads the neural network to converge at a unique optimal state; 2) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions (PD; the direction in which the neuron is maximally active) of M1 neurons; and 3) the bias of the PDs is steadily formed during the minimization of the motor effort. Furthermore, using a non-linear network model with realistic musculoskeletal data, we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks, including two-dimensional isometric torque production, two-dimensional reaching, and even three-dimensional reaching tasks. These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control.

Dataset Information

Baseline sensorimotor GABA levels shape neuroplastic processes induced by motor learning in older adults.

Publications

Baseline sensorimotor GABA levels shape neuroplastic processes induced by motor learning in older adults.

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