Right prefrontal cortex transcranial direct current stimulation enhances multi-day savings in sensorimotor adaptation

Seidler, Rachael D.; Gluskin, Brittany S.; Greeley, Brian

doi:10.1152/jn.00563.2016

Cited by 32 publications

(17 citation statements)

References 36 publications

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“…Although we found evidence for overlapping neural processes between late adaptation and savings, there are reports in the literature supporting that savings is more associated with early learning cognitive/strategic processes (Haith et al, 2015;Morehead et al, 2015;Seidler, Gluskin, & Greeley, 2017). One potential explanation for these differential findings is that these previous studies were based on behavioral measures of adaptation (Haith et al, 2015;Morehead et al, 2015;Seidler et al, 2017).…”

Section: Fig Urecontrasting

confidence: 66%

Neural predictors of sensorimotor adaptation rate and savings

Cassady

Ruitenberg²,

Koppelmans³

et al. 2017

Human Brain Mapping

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In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations.

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Section: Fig Urecontrasting

confidence: 66%

Neural predictors of sensorimotor adaptation rate and savings

Cassady

Ruitenberg²,

Koppelmans³

et al. 2017

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Remarkably, participants who showed more strengthening of the sensorimotor network demonstrated less savings. In another recent study 8 transcranial direct current stimulation (tDCS) was employed to evaluate whether the right and left dorsolateral prefrontal cortex (DLPFC) and left M1 contribute to savings. Participants adapted dart-throwing movements while wearing laterally displacing prism lenses and while receiving anodal tDCS to one of the aforementioned sites.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the right DLPFC has been linked with working memory contributions to early adaptation 1 , 4 and selection of goal-directed actions 25 . Indeed, recent findings suggest a role for the right DLPFC in savings 8 . Furthermore, previous studies have shown the anterior cingulate cortex (ACC) is recruited during early adaptation 21 and plays a role in assigning value to actions on the basis of relevant feedback 26 .…”

Section: Introductionmentioning

confidence: 99%

Neural correlates of multi-day learning and savings in sensorimotor adaptation

Ruitenberg

Koppelmans

Dios

et al. 2018

Sci Rep

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In the present study we evaluated changes in neural activation that occur over the time course of multiple days of sensorimotor adaptation, and identified individual neural predictors of adaptation and savings magnitude. We collected functional MRI data while participants performed a manual adaptation task during four separate test sessions over a three-month period. This allowed us to examine changes in activation and associations with adaptation and savings at subsequent sessions. Participants exhibited reliable savings of adaptation across the four sessions. Brain activity associated with early adaptation increased across the sessions in a variety of frontal, parietal, cingulate, and temporal cortical areas, as well as various subcortical areas. We found that savings was positively associated with activation in several striatal, parietal, and cingulate cortical areas including the putamen, precuneus, angular gyrus, dorsal anterior cingulate cortex (dACC), and cingulate motor area. These findings suggest that participants may learn how to better engage cognitive processes across days, potentially reflecting improvements in action selection. We propose that such improvements may rely on action-value assignments, which previously have been linked to the dACC and striatum. As correct movements are assigned a higher value than incorrect movements, the former are more likely to be performed again.

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“…Another possibility is the application of non-791 invasive brain stimulation, which is a technique that appears to be suited to restore some of the age-792 related deficits (Hardwick and Celnik, 2014;Orban de Xivry and Shadmehr, 2014;Grimaldi et al, 2016). 793 A two-fold approach is possible to restore the decline of explicit strategy in elderly: Either directly, by 794 stimulation of a region such as dorsolateral prefrontal cortex that might temporary boost some 795 working memory resources (Seidler et al, 2017), or indirectly, by stimulation of the cerebellum that 796 might boost, the already intact, implicit component of motor adaptation. Finally, we expect that the 797 observed age-related decline of explicit strategy is having widespread consequences on other features 798 and components of motor adaptation such as reduced generalization, increased interference of motor 799 memories, reduced savings and reduced reinforcement learning.…”

Section: Discussionmentioning

confidence: 99%

Why is the explicit component of motor adaptation limited in elderly adults?

Xivry

Vandevoorde

2019

Preprint

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10The cognitive component of motor adaptation declines with aging. Yet, in other motor tasks, older 11 adults appear to rely on cognition to improve their motor performance. It is unknown why older adults 12 are not able to do so in motor adaptation. In order to solve this apparent contradiction, we tested the 13 possibility that older adults require more cognitive resources in unperturbed reaching compared to 14 younger adults, which leaves fewer resources available for the cognitive aspect of motor adaptation. 15

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Right prefrontal cortex transcranial direct current stimulation enhances multi-day savings in sensorimotor adaptation

Cited by 32 publications

References 36 publications

Neural predictors of sensorimotor adaptation rate and savings

Neural predictors of sensorimotor adaptation rate and savings

Neural correlates of multi-day learning and savings in sensorimotor adaptation

Why is the explicit component of motor adaptation limited in elderly adults?

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