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Using TMS to Increase Executive Function in Older Adults

Duke University · 2016
ClinicalTrials.gov · Datasets · License: Public Domain · 2016
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Cognitive decline and dementia have become important public health issues in our time as medical science has increased lifespan and our society becomes progressively older. A great deal of the cognitive decline due to aging can be explained by decline in working memory (WM), a mental function central to cognition in which aging deficits appear almost universally. Attempts to use WM training to increase WM ability in older adults has had some success, but the transfer of performance enhancements caused by this training to other cognitive skills is controversial. Another intervention that shows much promise is noninvasive stimulation of cerebral cortex using transcranial magnetic stimulation (TMS), which has been shown to increase performance in many cognitive tasks. Here, the investigator proposes to use fMRI-guided rTMS to enhance working memory performance. This will be achieved through three Aims. In the first, registered on this record, the investigator will stimulate both old and young healthy adults while they perform the WM task that will engage the frontoparietal network. To define the optimal rTMS target, rTMS will be applied over the dorsolateral prefrontal cortex (DLPFC: Aim 1a); or over the parietal cortex (PC: Aim 1b). These regions are involved not only in the maintenance of items in WM, but also in their manipulation, therefore applying rTMS over these areas should create WM performance enhancements that will be long-lasting. In Aim 1c, a direct within-subject comparison of these 2 targeted sites is performed. In the second and third Aims, older adults will receive active or sham rTMS over the optimal target (defined in Arm 1) during two weeks of daily sessions while they perform the WM tasks. In the second Aim, the investigator hopes to demonstrate that the cumulative effect of multiple TMS sessions, in tandem with the synergistic effects of simultaneous TMS + WM training, create WM performance enhancements greater than those found with WM training
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