Mathematician. Postdoctoral fellow with Jason Moore.
May 21st, 2019
1. extract image-related features
2. train model on normative dataset to predict age
3. obtain predicted ages on test set
brain-predicted age difference
5. make inference: accelerated/
BAG (brain age gap) = predicted age - real age
brainPAD (brain-predicted age difference)
brainAGE (brain age gap estimate)
BAG ~ MS diagnosis
longitudinal analysis within MS
(360 ROIs: thickness, area, volume)
10-fold CV r = 0.91
Four types of Multiple Sclerosis (MS)
rate of aging
Increased brain age gaps for all brain regions except temporal
Significant accelerated rate of brain aging compared to chronological aging
apparent accelerated aging of the brain may partly be explained by chronic inflammatory processes that drive neurodegeneration in MS
- circularity in the analysis of brain atrophy vs brain aging?
We estimated the annual global brain atrophy by comparing estimated total brain volume from the Freesurfer output (BrainSegVolNotVent) between time points.
- Estimation model accuracy: r = 0.91, what about MAE, MSE, etc.?
By Trang Le