Aging, Brain, Cognitive Dysfunction, Neurodegenerative Diseases
Conditions
Keywords
MRI, Brain age, Deep learning, Healthy longevity, Resilence, Cognitive maintenance, Simulation, Imaging phenotypes
Brief summary
Multimodal magnetic resonance imaging (MRI), detecting brain structural and functional changes, has emerged as a powerful and promising technique to study individual's brain, as T1-weighted scans can detect morphometric features, diffusion tensor imaging (DTI) scans can quantify structural connectivity, and functional MRI can capture the features of functional connectivity. Notably, with the advances in quantitative methods, computational models of brain age and brain-predicted age difference (brain-PAD) detecting the ageing effects on individual's brain features are becoming increasingly popular in clinical studies, which might revolutionize the diagnostic and prognostic phonotypes of age-related brain diseases globally.
Detailed description
The score of brain-PAD has threefold explanations: a. negative score representing decelerated brain ageing (brain age\<chronological age); b. positive score representing accelerated brain ageing (brain age\>chronological age); c. score equal to zero, representing normal brain ageing (brain age=chronological age). The score of brain-PAD indicates the brain ageing pattern with the interaction of lifestyle and cognitive status at individual level. For example, based on structural MRI scans, a younger brain age or a negative score of brain-PAD was found to be associated with better cognition, which indicates the potential utilities of brain age matrices in predicting individual's cognitive maintenance and healthy longevity
Interventions
None listed
Sponsors
Chinese University of Hong Kong
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 90 Years
Healthy volunteers
Yes
Inclusion criteria
* Chinese old adults are the ones who are over 60 years of chronological age. * Sleep disturbance: individual's subjective sleep quality is assessed using the Pittsburgh Sleep Quality Index (PSQI) with a total score \>5.
Exclusion criteria
* History of bipolar disorders or psychosis. * History of major neurological deficits, including stroke, transient ischemic attack or brain tumor. * Unable to participant magnetic resonance imaging (MRI) scanning.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Estimated brain age | Baseline | Using the structural and functional connectivity, individual's brain age is calculated based on the connectivity features of core brain networks. |
| Cognitive maintenance | Baseline | The dynamic change of cognitive status is used to evaluate cognitive maintenance. The performance of global cognition is used to determine the cognitive status, including mild neurocognitive disorder (NCD) and major NCD. The global cognition is measured by mini-mental state examination (MMSE). |
| Plasma β-amyloid | Baseline | The levels of Aβ42 and Aβ40 in the plasma samples will be quantified by enzyme-linked immunosorbent assay. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Brain-predicted age difference (brain-PAD) | Baseline | The brain-PAD score is computed as the difference between estimated brain age and chronological age (brain age minus chronological age). |
| Executive function | Baseline | Executive function is measured by the category verbal fluency test (CVFT). |
| Complex attention | Baseline | Attention network test (ANT) is an individually administered computerized task designed to evaluate three attentional components. |
| Memory and learning | Baseline | Word list learning test consisting of fifteen semantically non-associated words that is presented consecutively over three trials of immediate recall and a 20-min delayed recall. |
Countries
Hong Kong
Outcome results
None listed