Online repetitive transcranial magnetic stimulation during working memory in younger and older adults: A randomized within-subject comparison.

Abstract

Working memory is the ability to perform mental operations on information that is stored in a flexible, limited capacity buffer. The ability to manipulate information in working memory is central to many aspects of human cognition, but also declines with healthy aging. Given the profound importance of such working memory manipulation abilities, there is a concerted effort towards developing approaches to improve them. The current study tested the capacity to enhance working memory manipulation with online repetitive transcranial magnetic stimulation in healthy young and older adults. Online high frequency (5Hz) repetitive transcranial magnetic stimulation was applied over the left dorsolateral prefrontal cortex to test the hypothesis that active repetitive transcranial magnetic stimulation would lead to significant improvements in memory recall accuracy compared to sham stimulation, and that these effects would be most pronounced in working memory manipulation conditions with the highest cognitive demand in both young and older adults. Repetitive transcranial magnetic stimulation was applied while participants were performing a delayed response alphabetization task with three individually-titrated levels of difficulty. The left dorsolateral prefrontal cortex was identified by combining electric field modeling to individualized functional magnetic resonance imaging activation maps and was targeted during the experiment using stereotactic neuronavigation with real-time robotic guidance, allowing optimal coil placement during the stimulation. As no accuracy differences were found between young and older adults, the results from both groups were collapsed. Subsequent analyses revealed that active stimulation significantly increased accuracy relative to sham stimulation, but only for the hardest condition. These results point towards further investigation of repetitive transcranial magnetic stimulation for memory enhancement focusing on high difficulty conditions as those most likely to exhibit benefits.

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Citation

Published Version (Please cite this version)

10.1371/journal.pone.0213707

Publication Info

Beynel, L, SW Davis, CA Crowell, SA Hilbig, W Lim, D Nguyen, H Palmer, A Brito, et al. (2019). Online repetitive transcranial magnetic stimulation during working memory in younger and older adults: A randomized within-subject comparison. PloS one, 14(3). p. e0213707. 10.1371/journal.pone.0213707 Retrieved from https://hdl.handle.net/10161/20730.

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Scholars@Duke

Davis

Simon Wilton Davis

Associate Professor in Neurology

My research centers around the use of structural and functional imaging measures to study the shifts in network architecture in the aging brain. I am specifically interested in changes in how changes in structural and functional connectivity associated with aging impact the semantic retrieval of word or fact knowledge. Currently this involves asking why older adults have particular difficulty in certain kinds of semantic retrieval, despite the fact that vocabularies and knowledge stores typically improve with age.

A second line of research involves asking questions about how this semantic system is organized in young adults, understanding which helps form a basis for asking questions about older adults. To what degree are these semantic retrieval processes lateralized? What cognitive factors affect this laterality? How are brain structures like the corpus callosum involved in mediating distributed activation patterns associated with semantic retrieval? 

Peterchev

Angel V Peterchev

Professor in Psychiatry and Behavioral Sciences

I direct the Brain Stimulation Engineering Lab (BSEL) which focuses on the development, modeling, and application of devices and paradigms for transcranial brain stimulation. Transcranial brain stimulation involves non-invasive delivery of fields (e.g., electric and magnetic) to the brain that modulate neural activity. It is widely used as a tool for research and a therapeutic intervention in neurology and psychiatry, including several FDA-cleared indications. BSEL develops novel technology such as devices for transcranial magnetic stimulation (TMS) that leverage design techniques from power electronics and computational electromagnetics to enable more flexible stimulus control, focal stimulation, and quiet operation. We also deploy these devices in experimental studies to characterize and optimize the brain response to TMS. Another line of work is multi-scale computational models that couple simulations of the electromagnetic fields, single neuron responses, and neural population modulation induced by electric and magnetic brain stimulation. These models are calibrated and validated with experimental neural recordings through various collaborations. Apart from understanding of mechanisms, we develop modeling, algorithmic, and targeting tools for response estimation, dose individualization, and precise localization of transcranial brain stimulation using advanced techniques such as artificial neural networks and machine learning. Moreover, BSEL is involved in the integration of transcranial brain stimulation with robotics, neuronavigation, intracranial electrophysiology recordings, and imaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), as well as the evaluation of the safety of device–device interactions, for example between transcranial stimulators and implants. Importantly, we collaborate widely with neuroscientists and clinicians within Duke and at other institutions to translate developments from the lab to research and clinical applications. For over 15 years, BSEL has been continuously supported with multiple NIH grants as well as funding by DARPA, NSF, Brain & Behavior Research Foundation, Coulter Foundation, Duke Institute for Brain Sciences, MEDx, Duke University Energy Initiative, and industry. Further, some of our technology has been commercialized, for example as ElevateTMS cTMS, or incorporated in free software packages, such as SimNIBS.


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