Trang Le
#math graduate. Postdoc fellow with Jason Moore.
1. extract image-related features
2. train model on normative dataset to predict age
3. obtain predicted ages on test set
4. compute
brain-predicted age difference (brainPAD)
5. make inference: accelerated/
delayed aging
brainPAD framework
\[brainPAD = \widehat{age}_{brain} − age_{chronological}\]
- physical fitness
- mortality risk in elderly participants
- cognitive performance
- of psychiatric disorders
- mild cognitive impairment and Alzheimer’s disease
~ brainAGE: Age Gap Estimate
select voxels/ROIs
structural MRI
predicted brain age
DNA-methylation
epigenetic age
- real age
- real age
automated ML
\Delta
epigenetic age
(regularized NPDR)
~ genetic variants
epigenetic age
\Delta
Dimensions and stages of aging
Aim 3
Aim 2
Aim 2 + 3
select CpG sites
time
automated ML
brain age
\Delta
brain age
\Delta
Aim 1
Develop and evaluate NPDR
Aim 1. Identify features influencing brainPAD, optimize age prediction
Aim 2. Explore different data types of ADNI
Aim 3. Build integrative methods to create new measures of clinically abnormal aging
y_i = \beta_0 + \beta_a a_i + \epsilon_i
d_{ij}(y) = \beta_0 + \beta_a d_{ij}(a) + \epsilon_{ij}
projected distances
nearest neighbors
attributes
d_{ij}(y) = \beta_0 + \beta_a d_{ij}(a) + \epsilon_{ij}
M_a \sim d_{ij}(a|y_i \neq y_j)
H_a \sim d_{ij}(a|y_i = y_j)
discrete outcome
e.g., y \in \{0, 1\}
| Supports regularization | No | No | Yes |
m = 200
p = 1000
10% functionally interacting
∗ NPDR 0.05 adjusted cutoffs
Biological
processes
Accelerated
aging
Disease progression
?
?
\(allele_i \) carrier
\(allele_i \) non-carrier
time
composite \(\Delta\) age
K99-2019
By Trang Le
K99-2019
Figures presented in my grant proposal.
