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The mechanism underlying memory impairment caused by white matter lesions of cerebral small vessel disease is still unclear. The disrupted synchronization of gamma oscillations in the prefrontal-hippocampal circuit is a potential key mechanism. Our study has demonstrated that white matter lesions lead to demyelination of the connection tracts between the prefrontal lobe and hippocampus, which is closely related to memory dysfunction. However, further studies are required to explore if these microstructural changes in white matter tracts influence memory function by affecting gamma oscillations. Thus, this project will use the previously established episodic memory task and event-related potential to determine the changes in gamma oscillations in the prefrontal-hippocampal circuit and the effects on memory encoding and retrieval. Combining multimodal imaging, we will explore the mediating role of white matter microstructure damage, and establish a machine learning prediction model for memory impairment. In addition, transcranial alternation current stimulation (tACS) will be used to investigate the mechanisms of memory improvement by regulating the prefrontal-hippocampal gamma oscillations. This project will clarify the neural oscillation mechanism underlying memory impairment caused by white matter lesions of cerebral small vessel disease, with the expectation of providing new predictive indicators and interventions.
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| Measure | Description | Time Frame |
|---|---|---|
| Overall cognitive function | Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MOCA) scale scores. | baseline and one-year follow-up |
| Measure | Description | Time Frame |
|---|---|---|
| Attention and executive functions | the scores are evaluated using the Clock Drawing Test (CDT), the Digit Span Test (DST), and the Trail Making Test (TMT). | Baseline and follow up |
| Language function |
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Elderly population
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Xuanwu Hospital Capital Medical University | Recruiting | Beijing | Beijing Municipality | 100053 | China |
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Boston Naming Test (BNT)
| Baseline and one-year follow up |
| Memory function | Digit Span Test (DST) and the WHO-UCLA Auditory Verbal Learning Test (AVLT). | Baseline and one-year follow up |
| Brain imaging data | Based on DTI data, the following metrics can be extracted: Fractional Anisotropy (FA) Mean Diffusivity (MD) Axial Diffusivity (AD) Radial Diffusivity (RD) | baseline and one-year follow-up |
| Electroencephalogram (EEG) data. | Based on EEG data, time-frequency analysis and cross-frequency coupling analysis can be performed to extract the following metrics: Theta Band Neural Oscillation Energy Theta-Gamma Coupling Strength | baseline and one-year follow-up |