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    Heng Zhang

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Brain functional network construction and feature recognition based on deep learning and simultaneous EEG – fMRI in Alzheimer’s disease

Student name:

Heng Zhang (PhD candidate)

Supervisors:

  1. Professor Simon Laws
  2. Professor Wei Wang
  3. Professor Shu-Hua Ma (Shantou University, China)

Summary of thesis:

Alzheimer’s disease (AD) is the leading cause of dementia, and its incidence will continue to increase over the next 30 years. Mild cognitive impairment (MCI) is a stage between dementia and healthy aging, and leads to an increased risk of dementia. Therefore, understanding differences among healthy aging, MCI and AD, has the potential to lead to early diagnosis and more timely interventions and in turn result in a reduction in the incidence of AD. Magnetic Resonance Imaging (MRI) is an important neuroimaging marker assisting in the diagnosis of MCI and AD, with a sensitivity and specificity of up to 80% for AD classification. Electroencephalography (EEG) is a widely used low-cost, non-invasive technology, with Theta frequency being the earliest and most sensitive EEG marker of AD. This study aims to use multimodal neurological functional imaging based on simultaneous EEG and functional MRI (simultaneous EEG-fMRI) to make full use of the high temporal resolution of EEG and the high spatial resolution of fMRI, to construct a brain network and combine it with deep learning to quantitatively analyze neuroimaging data, so as to provide an effective basis for the clinical diagnosis of patients with cognitive impairment. This project has the potential to further improve the recognition rate and diagnostic accuracy in different stages of AD.

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