Alzheimer's disease (AD) is a common neurodegenerative disease in the aged population, which is clinically manifested as progressive memory disorder, aphasia, agnosia, visual spatial skill damage, executive function disorder, personality behavior changes and other comprehensive dementia. Mild cognitive impairment (MCI) is a condition of reduced cognitive function between AD and health. At present, MCI is considered to be the early stage of dementia and has a high risk of developing into dementia. In particular, amnestic mild cognitive impairment (aMCI) often turns into AD. Therefore, early identification and treatment will help delay cognitive deterioration.
The levels of Aß42, T-Tau and P-Tau in cerebrospinal fluid are three important biomarkers for the recognition of MCI. However, cerebrospinal fluid testing is invasive and has certain damage to the human body. Although the diagnosis is reliable, it is generally difficult for patients to accept. Studies have shown that gene expression data in the blood can also be used to recognize MCI. Researchers have found that gene expression in peripheral blood is partially correlated with gene expression in brain tissue, and differentially expressed genes in the blood can serve as surrogate markers for brain lesions to some extent. In addition to gene expression analysis, noninvasive neuroimaging techniques, such as PET, MRI, DTI, and fMRI are also widely used for MCI recognition. For example, PET can be used to measure cerebral cortex metabolism, DTI can be used to detect the direction of white matter nerve fibers by tracking the Brownian motion of water molecules.
With the development of Artificial Intelligence (AI), novel imaging analysis and gene data mining techniques could also be applied to further accelerate the exploration of MCI mechanisms. Therefore, the objective of this topic is to explore novel methods, including imaging analysis and gene data mining, for recognizing MCI.
The sub-topics of interest include, but are not limited to:
- Differential gene expression analysis, clustering analysis, classification analysis, principal component analysis, etc.
- Neuroimage registration of different modes.
- Radiomics-based neuroimage analysis.
- Gene expression data and neuroimage data fusion.
- Deep learning-based gene differential expression and neuroimage analysis.
Alzheimer's disease (AD) is a common neurodegenerative disease in the aged population, which is clinically manifested as progressive memory disorder, aphasia, agnosia, visual spatial skill damage, executive function disorder, personality behavior changes and other comprehensive dementia. Mild cognitive impairment (MCI) is a condition of reduced cognitive function between AD and health. At present, MCI is considered to be the early stage of dementia and has a high risk of developing into dementia. In particular, amnestic mild cognitive impairment (aMCI) often turns into AD. Therefore, early identification and treatment will help delay cognitive deterioration.
The levels of Aß42, T-Tau and P-Tau in cerebrospinal fluid are three important biomarkers for the recognition of MCI. However, cerebrospinal fluid testing is invasive and has certain damage to the human body. Although the diagnosis is reliable, it is generally difficult for patients to accept. Studies have shown that gene expression data in the blood can also be used to recognize MCI. Researchers have found that gene expression in peripheral blood is partially correlated with gene expression in brain tissue, and differentially expressed genes in the blood can serve as surrogate markers for brain lesions to some extent. In addition to gene expression analysis, noninvasive neuroimaging techniques, such as PET, MRI, DTI, and fMRI are also widely used for MCI recognition. For example, PET can be used to measure cerebral cortex metabolism, DTI can be used to detect the direction of white matter nerve fibers by tracking the Brownian motion of water molecules.
With the development of Artificial Intelligence (AI), novel imaging analysis and gene data mining techniques could also be applied to further accelerate the exploration of MCI mechanisms. Therefore, the objective of this topic is to explore novel methods, including imaging analysis and gene data mining, for recognizing MCI.
The sub-topics of interest include, but are not limited to:
- Differential gene expression analysis, clustering analysis, classification analysis, principal component analysis, etc.
- Neuroimage registration of different modes.
- Radiomics-based neuroimage analysis.
- Gene expression data and neuroimage data fusion.
- Deep learning-based gene differential expression and neuroimage analysis.
