Subjective cognitive decline (SCD) is a common symptom in the aged population that seemingly healthy older individuals report a self-perceived decline in cognition function, usually memory loss, without objective evidence from formal neuropsychological tests. SCD is a form of cognitive impairment and a growing body of evidence shows that it may be one of the earliest noticeable symptoms of Alzheimer’s disease (AD) and related dementias. Epidemiological data also suggest that individuals with subjective cognitive decline are at greater risk of progression to AD dementia. However, SCD is yet to become a reliable marker for preclinical AD diagnoses. Moreover, it is also important to explore prediction indexes for patients with high progression risks from SCD to cognitive impairment in order to provide early therapeutic interventions.
The development of neuroimaging techniques has provided the potential to explore the pathology of AD. For example, the brain atrophy in structural magnetic resonance imaging (sMRI), abnormal oxygen consumption in functional magnetic resonance imaging (fMRI), or connection loss in diffusion tensor imaging (DTI) are all associated with the pathology of AD. From the perspective of molecular mechanisms, it is believed that the changes in molecular characterizations, such as glucose metabolism, common or distinctive biomarkers such as Amyloid-Beta, Tau protein, and Apoe4 are also associated with the pathology of AD.
In addition, with the development of Artificial Intelligence (AI), novel imaging analysis and data mining computational techniques could also be applied to further accelerate the exploration of AD mechanism.
Therefore, the objective of this topic is to explore reliable markers and prediction indexes for the progression of SCD from multidisciplinary perspectives including neuroimaging techniques, genetic or inflammation mechanisms, as well as AI applications.
The sub-topics include, but are not limited to:
1. Innovative experimental research of emerging interests in SCD involving distinctive biomarkers using neuroimaging techniques (sMRI, fMRI, DTI and molecular imaging)
2. Exploration of the risk factors and/or protective factors associated with the progression from SCD to cognitive impairment through epidemiological methods
3. Clinical applications of new techniques and methods to predict the progression from SCD to cognitive impairment
4. Clinical applications of computer-aided diagnostic approaches based on AI with multi-modal medical information to predict the progression from SCD to cognitive impairment
5. Application of bioinformatics and computational biology in big data mining that analyzes a specific database or medical records system to investigate SCD
Subjective cognitive decline (SCD) is a common symptom in the aged population that seemingly healthy older individuals report a self-perceived decline in cognition function, usually memory loss, without objective evidence from formal neuropsychological tests. SCD is a form of cognitive impairment and a growing body of evidence shows that it may be one of the earliest noticeable symptoms of Alzheimer’s disease (AD) and related dementias. Epidemiological data also suggest that individuals with subjective cognitive decline are at greater risk of progression to AD dementia. However, SCD is yet to become a reliable marker for preclinical AD diagnoses. Moreover, it is also important to explore prediction indexes for patients with high progression risks from SCD to cognitive impairment in order to provide early therapeutic interventions.
The development of neuroimaging techniques has provided the potential to explore the pathology of AD. For example, the brain atrophy in structural magnetic resonance imaging (sMRI), abnormal oxygen consumption in functional magnetic resonance imaging (fMRI), or connection loss in diffusion tensor imaging (DTI) are all associated with the pathology of AD. From the perspective of molecular mechanisms, it is believed that the changes in molecular characterizations, such as glucose metabolism, common or distinctive biomarkers such as Amyloid-Beta, Tau protein, and Apoe4 are also associated with the pathology of AD.
In addition, with the development of Artificial Intelligence (AI), novel imaging analysis and data mining computational techniques could also be applied to further accelerate the exploration of AD mechanism.
Therefore, the objective of this topic is to explore reliable markers and prediction indexes for the progression of SCD from multidisciplinary perspectives including neuroimaging techniques, genetic or inflammation mechanisms, as well as AI applications.
The sub-topics include, but are not limited to:
1. Innovative experimental research of emerging interests in SCD involving distinctive biomarkers using neuroimaging techniques (sMRI, fMRI, DTI and molecular imaging)
2. Exploration of the risk factors and/or protective factors associated with the progression from SCD to cognitive impairment through epidemiological methods
3. Clinical applications of new techniques and methods to predict the progression from SCD to cognitive impairment
4. Clinical applications of computer-aided diagnostic approaches based on AI with multi-modal medical information to predict the progression from SCD to cognitive impairment
5. Application of bioinformatics and computational biology in big data mining that analyzes a specific database or medical records system to investigate SCD
