Alzheimer Disease, Late Onset, Mild Cognitive Impairment, Subjective Cognitive Decline
Conditions
Keywords
magnetic resonance imaging, spatial navigation, olfaction, genetic, plasma biomarkers
Brief summary
To establish the diagnostic and prognostic models that could help the preclinical identification of subjects at higher risk of clinical progression to mild cognitive impairment and dementia based on combined features of baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data.
Detailed description
Alzheimer's disease (AD) is a global concern. Due to the lack of effective therapeutic methods targeting late-stage AD patients, it is critical to investigate brain alterations in the preclinical stage to pave the way for early diagnosis and intervention. Structural and functional magnetic resonance imaging (MRI) has been proven to be an effective and non-invasive approach to explore the neural mechanisms underlying neurological disorders. Genetic factors such as apolipoprotein E and plasma biomarkers play important roles in AD development and progression. However, the interaction effects of risk genes and different pathologic pathways implicated in the pathogenesis of AD remain unclear. Furthermore, the diagnostic and prognostic models that could predict future cognitive decline or clinical progression based on objective features derived from baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data need to be further explored. We aim to investigate the neural basis underlying early cognitive deficits using structural and functional MRI data combined with novel analytical methods such as dynamic functional connectivity, surface-based morphometry, graph theory, multilayer network, functional-structural coupling, hidden Markov model, and connectome gradient mapping. Secondly, to explore the interaction effects of risk genes, which may help a better illustration of different biological pathways implicated in the pathogenesis of Alzheimer's disease. Thirdly, to investigate the divergent and dynamic abnormalities of multimodal imaging markers across different stages of Alzheimer's disease and their associations with plasma biomarkers, which may enhance our understanding of the neuropathological mechanisms. Fourthly, to provide scientific evidence on the potential targets for early intervention of neurodegenerative diseases. Lastly, to establish the diagnostic and prognostic models that could help the preclinical identification of subjects at higher risk of clinical progression to mild cognitive impairment and dementia based on combined features of baseline multimodal biomarkers. These studies may help a better understanding of the neural and biological basis underlying AD and pave the way for early diagnosis and intervention.
Interventions
OTHERMultimodal magnetic resonance imaging scanning, behavioral, genetic and plasma biomarker testing
Multimodal magnetic resonance imaging scanning, including 3DT1, 3DT2, 3DFLAIR, functional MRI, DTI, NODDI, ASL, QSM behavioral testing, such as olfaction and spatial navigation genetic testing, such as APOE, BDNF plasma biomarker testing, such as ptau, Aβ42/40、NfL、GFAP
Sponsors
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 79 Years
Healthy volunteers
Yes
Inclusion criteria
* The inclusion criteria were 50-79 years old and having 8 or more years of education.
Exclusion criteria
* Participants with a history of stroke, other neurological disorders that could lead to cognitive impairment (Parkinson's disease, encephalitis, epilepsy, brain tumors, etc.), severe anxiety or depression, and contraindications for magnetic resonance imaging (MRI) were not enrolled.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| the area under the curve of the classification analysis between progressors and nonprogressors | Baseline, Year 1, Year 2, Year 3 | We'll measure the area under the curve of the ROC curves based on combined features of baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data in discriminating those convert to MCI or AD (progressors) from those do not convert (nonprogressors) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| mediation effects of MRI on the associations between gene/plasma biomarker and cognition/behavior | Baseline | We'll explore whether MRI features could act as mediators between genetic factors and cognition or behavior, as well as between plasma biomarkers and cognition or behavior. |
Countries
China
Outcome results
None listed