Neuromodulation of Memory in Aging
Purpose
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.
Conditions
- Mild Cognitive Impairment
- MCI
Eligibility
- Eligible Ages
- Between 18 Years and 75 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- English Speaking - Willing to provide consent
Exclusion Criteria
- History of any Axis I DSM-V disorder, excluding major depressive disorder or generalized anxiety disorders - Current history of substance abuse or dependence (excluding nicotine) - Intracranial implants (e.g. aneurysms clips, shunts, stimulators, cochlear implants, or electrodes), cardiac pacemakers, or vagus Nerve stimulation device - Increased risk of seizure for any reason, including prior diagnosis of epilepsy, seizure disorder, increased intracranial pressure, or history of significant head trauma with loss of consciousness for ≥ 5 minutes. - Neurological disorder including, but not limited to: space occupying brain lesion; any history of seizures, history of cerebrovascular accident; fainting, cerebral aneurysm, Dementia, Hungtington chorea; Multiple Sclerosis. - Current use of medications known to lower the seizure threshold and/or affect working memory
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Non-Randomized
- Intervention Model
- Single Group Assignment
- Intervention Model Description
- The study has three Aims and thus three models. For Aim 1, the primary outcome measure is fMRI BOLD. Researchers will estimate the dose-response relationship between TMS intensity and BOLD response in two cortical sites for response to TMS. For Aim 2 the primary outcome measure is impact of closed-loop TMS treatment on working memory task performance. For Aim 3, the primary outcome measure is the strength of mediation of brain health (e.g., vascular density) predictors of cognition.
- Primary Purpose
- Basic Science
- Masking
- Single (Participant)
- Masking Description
- This is a within-subjects design, such that both active and sham stimulation (i.e., masking) trials will occur in all subjects.
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
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Experimental TMS-Randomized |
Three different closed-loop conditions will be tested, each triggered by the presence of a sustained period of alpha-band power. In the first condition, arrhythmic TMS trains with a stochastic (randomized) inter-pulse interval, will be used to disrupt cortical alpha oscillations and thus be expected to enhance memory performance. |
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Experimental TMS-Ordered |
Three different closed-loop conditions will be tested, each triggered by the presence of a sustained period of alpha-band power. In the second condition, rhythmic (ordered) alpha-frequency TMS trains, with the expectation that this alpha stimulation will further entrain a synchronization during the task and thereby worsen memory performance. |
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Sham Comparator TMS-Sham |
Three different closed-loop conditions will be tested, each triggered by the presence of a sustained period of alpha-band power. In a third condition, sham stimulation will be delivered at the same randomized inter-pulse interval, but with no TMS delivered to the brain. |
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Recruiting Locations
Durham, North Carolina 27710
More Details
- Status
- Recruiting
- Sponsor
- Indiana University
Detailed Description
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.