Pre-clinical Alzheimer's disease diagnosis using non-invasive neuroimaging tools

Jinren Yu

doi:10.1117/12.2669353

24 March 2023 Pre-clinical Alzheimer's disease diagnosis using non-invasive neuroimaging tools

Jinren Yu

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126110J (2023) https://doi.org/10.1117/12.2669353
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

There is still much work to do to improve the accuracy of AD diagnosis. There is much biometrics that is helpful for AD identification. This study focuses on determining whether brain wave signals collected at sleep state can be used for AD diagnosis and proceeds our study to find proper bio metrics that are applicable for pre-clinical diagnosis. And this research aims to testify the validity of the model designed. The author recruits 50 patients aged from 60 to 80. All the participants go through PET scannings and cognitive tests to identify their MCI/AD progression. The non-invasive neuroimaging tools ( like EEG and rsfMRI) are used to record the patient brain signals. After pre-processing, the author calculate the bio-metrics based on the data we collected. Moreover, the local regression was adopted to analyze the relationship between the bio metrics and AD progression, which is evaluated by PET images and cognitive test results. The author also applies mixed-effect modeling to reduce the error created by random factors like age and gender. This essay expects that there are a close relationship between these bio-metrics and AD pathology. Also, the author plans to develop a pre-clinical AD diagnosis procedure by using non-invasive neuroimaging equipment.

Citation Download Citation

Jinren Yu "Pre-clinical Alzheimer's disease diagnosis using non-invasive neuroimaging tools", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126110J (24 March 2023); https://doi.org/10.1117/12.2669353

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KEYWORDS

Electroencephalography

Alzheimer's disease

Brain

Functional magnetic resonance imaging

Neuroimaging

Brain diseases

Neurodegeneration

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