Dementia due to Alzheimer's disease (AD) is a leading public health concern in the US with enormous care costs and no effective pharmacotherapy despite multiple clinical trials. Multiple studies have shown mild cognitive impairment (MCI) to be a precursor risk for AD and to be more amenable to intervention. While preclinical studies have shown that directly modulating activity in the prefrontal cortex (PFC) using non-invasive brain stimulation techniques, such as transcranial magnetic stimulation (TMS), can modulate cognitive function in healthy older adults, there is little evidence of reliable efficacy in MCI.~The investigators posit three reasons for this lack of efficacy. First, there is no established means of estimating a reliable biomarker and unique dose-response relationship between TMS intensity and brain activity. Second, standard TMS protocols fail to capture the dynamic nature of cognitive states and the reaction of endogenous brain states to exogenous neuromodulation. Third, no studies using TMS in AD-related populations have accounted for the influence of cerebrovascular disease in the response to TMS. The investigators propose to address these shortcomings by using closed-loop TMS, based on individualized brain networks to establish parameters that can reliably control brain states during normal memory functioning in healthy aging and MCI.~To achieve this goal, the investigators will study network activation and neural oscillatory mechanisms underlying the network that regulates working memory (WM), a cognition function with a reliable prefrontal cortex (PFC) network characterization. The investigators will then target this network using closed-loop TMS to the PFC and measure the impact on WM performance and task-based neural activity. This approach uses concurrent TMS-fMRI to identify dose-response relationships in the working memory network. Next, the investigators apply novel closed-loop TMS to perturb this network using temporally-precise TMS-EEG. Lastly, the investigators will integrate information collected via fMRI and EEG into a single computational framework to model spatiotemporal dynamics of the global brain network and predict the success of the TMS-related response in our MCI cohort. The project will use cutting-edge brain stimulation and network modeling techniques to enhance WM in healthy older adults and MCI and will provide a demonstration of the value of closed-loop, network-guided TMS for future clinical applications.
The proposed research will use closed-loop transcranial magnetic stimulation (TMS) based on individualized brain networks to establish parameters that can reliably control brain states. This will be tested in healthy aging and mild cognitive impairment (MCI) cohorts. The investigators will study network activation and neural oscillatory mechanisms underlying the network that regulates working memory and then target this network using closed-loop TMS to the Prefrontal Cortex. Investigators will measure the impact of TMS on working memory performance and task-based neural activity. The project will use brain stimulation and network modeling techniques to enhance working memory in healthy older adults and MCI and will demonstrate the value of closed-loop, network-guided TMS for future clinical applications.